From 363ca42cdf36a1b8bbd3fc85280e9119ec951f4f Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Wed, 22 May 2024 11:04:50 -0400 Subject: [PATCH 01/38] Adds set-of-marks rendering for burners in the kitchen environment (#1654) * good to go! * mypy --- predicators/envs/kitchen.py | 40 +++++++++++++++++++++++++++++----- predicators/settings.py | 1 + predicators/utils.py | 43 +++++++++++++++++++++++++++++++++++++ 3 files changed, 79 insertions(+), 5 deletions(-) diff --git a/predicators/envs/kitchen.py b/predicators/envs/kitchen.py index 0329951442..20ca492405 100644 --- a/predicators/envs/kitchen.py +++ b/predicators/envs/kitchen.py @@ -5,7 +5,9 @@ import matplotlib import numpy as np +import PIL from gym.spaces import Box +from PIL import ImageDraw try: import gymnasium as mujoco_kitchen_gym @@ -183,8 +185,35 @@ def render(self, action: Optional[Action] = None, caption: Optional[str] = None) -> Video: assert caption is None - arr: Image = self._gym_env.render() # type: ignore - return [arr] + curr_img_arr: Image = self._gym_env.render() # type: ignore + if CFG.kitchen_render_set_of_marks: + # Add text labels for the burners to the image. Useful for VLM-based + # predicate invention. + curr_img_pil = PIL.Image.fromarray(curr_img_arr) # type: ignore + draw = ImageDraw.Draw(curr_img_pil) + # Specify the font size and type (default font is used here) + font = utils.get_scaled_default_font(draw, 3) + # Define the text and position + burner1_text = "burner1" + burner1_position = (300, 290) + burner2_text = "burner2" + burner2_position = (210, 310) + burner3_text = "burner3" + burner3_position = (260, 235) + burner4_text = "burner4" + burner4_position = (185, 245) + burner1_img = utils.add_text_to_draw_img(draw, burner1_position, + burner1_text, font) + burner2_img = utils.add_text_to_draw_img(burner1_img, + burner2_position, + burner2_text, font) + burner3_img = utils.add_text_to_draw_img(burner2_img, + burner3_position, + burner3_text, font) + _ = utils.add_text_to_draw_img(burner3_img, burner4_position, + burner4_text, font) + curr_img_arr = np.array(curr_img_pil) + return [curr_img_arr] @property def predicates(self) -> Set[Predicate]: @@ -276,7 +305,7 @@ def step(self, action: Action) -> Observation: self._gym_env.render() self._current_observation = { "state_info": self.get_object_centric_state_info(), - "obs_images": [self._gym_env.render()] + "obs_images": self.render() } return self._copy_observation(self._current_observation) @@ -377,7 +406,7 @@ def _reset_initial_state_from_seed(self, seed: int) -> Observation: self._gym_env.reset(seed=seed) return { "state_info": self.get_object_centric_state_info(), - "obs_images": [self._gym_env.render()] + "obs_images": self.render() } @classmethod @@ -559,7 +588,8 @@ def _KettleBoiling_holds(cls, state: State, """Predicate that's necessary for goal specification.""" kettle, burner, knob = objects return cls.On_holds(state, [knob]) and cls._OnTop_holds( - state, [kettle, burner]) + state, [kettle, burner]) and cls._KnobAndBurnerLinkedHolds( + state, [knob, burner]) @classmethod def _KnobAndBurnerLinkedHolds(cls, state: State, diff --git a/predicators/settings.py b/predicators/settings.py index 3833f1ceb4..0f3c31c884 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -322,6 +322,7 @@ class GlobalSettings: # kitchen env parameters kitchen_use_perfect_samplers = False kitchen_goals = "all" + kitchen_render_set_of_marks = False # sticky table env parameters sticky_table_num_tables = 5 diff --git a/predicators/utils.py b/predicators/utils.py index d4f4887a4c..04f53b18f5 100644 --- a/predicators/utils.py +++ b/predicators/utils.py @@ -37,6 +37,7 @@ from gym.spaces import Box from matplotlib import patches from numpy.typing import NDArray +from PIL import ImageDraw, ImageFont from pyperplan.heuristics.heuristic_base import \ Heuristic as _PyperplanBaseHeuristic from pyperplan.planner import HEURISTICS as _PYPERPLAN_HEURISTICS @@ -3791,3 +3792,45 @@ def run_ground_nsrt_with_assertions(ground_nsrt: _GroundNSRT, assert not atom.holds(state), \ f"Delete effect for {ground_nsrt_str} failed: {atom}" return state + + +def get_scaled_default_font( + draw: ImageDraw.ImageDraw, + size: int) -> ImageFont.FreeTypeFont: # pragma: no cover + """Method that modifies the size of some provided PIL ImageDraw font. + + Useful for scaling up font sizes when using PIL to insert text + directly into images. + """ + # Determine the scaling factor + base_font = ImageFont.load_default() + width, height = draw.textbbox((0, 0), "A", font=base_font)[:2] + scale_factor = size / max(width, height) + # Scale the font using the factor + return base_font.font_variant(size=int(scale_factor * # type: ignore + base_font.size)) # type: ignore + + +def add_text_to_draw_img( + draw: ImageDraw.ImageDraw, position: Tuple[int, int], text: str, + font: ImageFont.FreeTypeFont +) -> ImageDraw.ImageDraw: # pragma: no cover + """Method that adds some text with a particular font at a particular pixel + position in an input PIL.ImageDraw.ImageDraw image. + + Returns the modified ImageDraw.ImageDraw with the added text. + """ + text_width, text_height = draw.textbbox((0, 0), text, font=font)[2:] + background_position = (position[0] - 5, position[1] - 5 + ) # Slightly larger than text + background_size = (text_width + 10, text_height + 10) + # Draw the background rectangle + draw.rectangle([ + background_position, + (background_position[0] + background_size[0], + background_position[1] + background_size[1]) + ], + fill="black") + # Add the text to the image + draw.text(position, text, fill="red", font=font) + return draw From 8f6dfe0b9958acfa875745423e16871d04bfc72f Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Thu, 23 May 2024 16:15:53 -0400 Subject: [PATCH 02/38] Making "combined" options and NSRT's for kitchen (#1655) * init commit (should work for kettle pushing and knob turning!) * should be good to go for now * mypy --- .../ground_truth_models/kitchen/nsrts.py | 160 +++++++++++++++--- .../ground_truth_models/kitchen/options.py | 86 +++++++++- predicators/settings.py | 1 + 3 files changed, 222 insertions(+), 25 deletions(-) diff --git a/predicators/ground_truth_models/kitchen/nsrts.py b/predicators/ground_truth_models/kitchen/nsrts.py index 397897e21b..94b58dcdc8 100644 --- a/predicators/ground_truth_models/kitchen/nsrts.py +++ b/predicators/ground_truth_models/kitchen/nsrts.py @@ -55,6 +55,8 @@ def get_nsrts(env_name: str, types: Dict[str, Type], TurnOnKnob = options["TurnOnKnob"] PushOpen = options["PushOpen"] PushClose = options["PushClose"] + PushKettleOntoBurner = options["PushKettleOntoBurner"] + MoveAndTurnOnKnob = options["MoveAndTurnOnKnob"] # Predicates AtPreTurnOn = predicates["AtPreTurnOn"] @@ -99,7 +101,6 @@ def moveto_preturnoff_sampler(state: State, goal: Set[GroundAtom], delete_effects, ignore_effects, option, option_vars, moveto_preturnoff_sampler) - nsrts.add(move_to_pre_turn_off_nsrt) # MoveToPreTurnOn parameters = [gripper, on_off_obj] @@ -125,7 +126,6 @@ def moveto_preturnon_sampler(state: State, goal: Set[GroundAtom], preconditions, add_effects, delete_effects, ignore_effects, option, option_vars, moveto_preturnon_sampler) - nsrts.add(move_to_pre_turn_on_nsrt) # MoveToPrePushOnTop parameters = [gripper, kettle] @@ -155,7 +155,6 @@ def moveto_prepushontop_sampler(state: State, goal: Set[GroundAtom], delete_effects, ignore_effects, option, option_vars, moveto_prepushontop_sampler) - nsrts.add(move_to_pre_push_on_top_nsrt) # MoveToPrePullKettle parameters = [gripper, kettle] @@ -184,7 +183,6 @@ def moveto_prepullkettle_sampler(state: State, goal: Set[GroundAtom], delete_effects, ignore_effects, option, option_vars, moveto_prepullkettle_sampler) - nsrts.add(move_to_pre_pull_kettle_nsrt) # PushObjOnObjForward parameters = [gripper, kettle, surface_from, surface_to] @@ -219,7 +217,35 @@ def push_obj_on_obj_forward_sampler(state: State, delete_effects, ignore_effects, option, option_vars, push_obj_on_obj_forward_sampler) - nsrts.add(push_obj_on_obj_forward_nsrt) + + # PushKettleOntoBurner + def move_and_push_kettle_sampler(state: State, goal: Set[GroundAtom], + rng: np.random.Generator, + objs: Sequence[Object]) -> Array: + move_sample = moveto_prepushontop_sampler(state, goal, rng, + objs[:2]) + push_sample = push_obj_on_obj_forward_sampler( + state, goal, rng, objs) + return np.concatenate([move_sample, push_sample], axis=0) + + parameters = [gripper, kettle, surface_from, surface_to] + preconditions = { + LiftedAtom(NotOnTop, [kettle, surface_to]), + LiftedAtom(BurnerAhead, [surface_to, surface_from]), + LiftedAtom(OnTop, [kettle, surface_from]), + } + add_effects = {LiftedAtom(OnTop, [kettle, surface_to])} + delete_effects = {LiftedAtom(NotOnTop, [kettle, surface_to])} + ignore_effects = { + AtPreTurnOn, AtPrePushOnTop, AtPreTurnOff, AtPrePullKettle + } + option = PushKettleOntoBurner + option_vars = [gripper, kettle, surface_to] + push_kettle_onto_burner_nsrt = NSRT("PushKettleOntoBurner", parameters, + preconditions, add_effects, + delete_effects, ignore_effects, + option, option_vars, + move_and_push_kettle_sampler) # PushObjOnObjForwardToBoilKettle parameters = [gripper, kettle, surface_from, surface_to, knob] @@ -241,13 +267,34 @@ def push_obj_on_obj_forward_sampler(state: State, } option = PushObjOnObjForward option_vars = [gripper, kettle, surface_to] - push_obj_on_obj_forward_nsrt = NSRT("PushObjOnObjForwardAndBoilKettle", - parameters, preconditions, - add_effects, delete_effects, - ignore_effects, option, - option_vars, - push_obj_on_obj_forward_sampler) - nsrts.add(push_obj_on_obj_forward_nsrt) + push_obj_on_obj_forward_and_boil_kettle_nsrt = NSRT( + "PushObjOnObjForwardAndBoilKettle", parameters, preconditions, + add_effects, delete_effects, ignore_effects, option, option_vars, + push_obj_on_obj_forward_sampler) + + # PushKettleOntoBurnerAndBoil + parameters = [gripper, kettle, surface_from, surface_to, knob] + preconditions = { + LiftedAtom(NotOnTop, [kettle, surface_to]), + LiftedAtom(BurnerAhead, [surface_to, surface_from]), + LiftedAtom(OnTop, [kettle, surface_from]), + LiftedAtom(TurnedOn, [knob]), + LiftedAtom(KnobAndBurnerLinked, [knob, surface_to]) + } + add_effects = { + LiftedAtom(OnTop, [kettle, surface_to]), + LiftedAtom(KettleBoiling, [kettle, surface_to, knob]) + } + delete_effects = {LiftedAtom(NotOnTop, [kettle, surface_to])} + ignore_effects = { + AtPreTurnOn, AtPrePushOnTop, AtPreTurnOff, AtPrePullKettle + } + option = PushKettleOntoBurner + option_vars = [gripper, kettle, surface_to] + push_kettle_onto_burner_and_boil_nsrt = NSRT( + "PushKettleOntoBurnerAndBoil", parameters, preconditions, + add_effects, delete_effects, ignore_effects, option, option_vars, + move_and_push_kettle_sampler) # PullKettle parameters = [gripper, kettle, surface_from, surface_to] @@ -279,7 +326,6 @@ def pull_kettle_sampler(state: State, goal: Set[GroundAtom], pull_kettle_nsrt = NSRT("PullKettle", parameters, preconditions, add_effects, delete_effects, ignore_effects, option, option_vars, pull_kettle_sampler) - nsrts.add(pull_kettle_nsrt) # TurnOffSwitch parameters = [gripper, switch] @@ -313,7 +359,6 @@ def switch_turn_sampler(state: State, goal: Set[GroundAtom], add_effects, delete_effects, ignore_effects, option, option_vars, switch_turn_sampler) - nsrts.add(turn_off_switch_nsrt) # TurnOnSwitch parameters = [gripper, switch] @@ -332,7 +377,6 @@ def switch_turn_sampler(state: State, goal: Set[GroundAtom], turn_on_switch_nsrt = NSRT("TurnOnSwitch", parameters, preconditions, add_effects, delete_effects, ignore_effects, option, option_vars, switch_turn_sampler) - nsrts.add(turn_on_switch_nsrt) # TurnOnKnob parameters = [gripper, knob] @@ -363,7 +407,30 @@ def knob_turn_on_sampler(state: State, goal: Set[GroundAtom], turn_on_knob_nsrt = NSRT("TurnOnKnob", parameters, preconditions, add_effects, delete_effects, ignore_effects, option, option_vars, knob_turn_on_sampler) - nsrts.add(turn_on_knob_nsrt) + + # MoveAndTurnOnKnob + parameters = [gripper, knob] + preconditions = {LiftedAtom(TurnedOff, [knob])} + add_effects = {LiftedAtom(TurnedOn, [knob])} + delete_effects = {LiftedAtom(TurnedOff, [knob])} + ignore_effects = { + AtPreTurnOn, AtPrePushOnTop, AtPreTurnOff, AtPrePullKettle + } + option = MoveAndTurnOnKnob + option_vars = [gripper, knob] + + def move_and_knob_turn_on_sampler(state: State, goal: Set[GroundAtom], + rng: np.random.Generator, + objs: Sequence[Object]) -> Array: + move_sample = moveto_preturnon_sampler(state, goal, rng, objs[:2]) + turn_on_sample = knob_turn_on_sampler(state, goal, rng, objs) + return np.concatenate([move_sample, turn_on_sample], axis=0) + + move_and_turn_on_knob_nsrt = NSRT("MoveAndTurnOnKnob", parameters, + preconditions, add_effects, + delete_effects, ignore_effects, + option, option_vars, + move_and_knob_turn_on_sampler) # TurnOnKnobAndBoilKettle parameters = [gripper, knob, surface_to, kettle] @@ -383,11 +450,34 @@ def knob_turn_on_sampler(state: State, goal: Set[GroundAtom], } option = TurnOnKnob option_vars = [gripper, knob] - turn_on_knob_nsrt = NSRT("TurnOnKnobAndBoilKettle", parameters, - preconditions, add_effects, delete_effects, - ignore_effects, option, option_vars, - knob_turn_on_sampler) - nsrts.add(turn_on_knob_nsrt) + turn_on_knob_and_boil_kettle_nsrt = NSRT("TurnOnKnobAndBoilKettle", + parameters, preconditions, + add_effects, delete_effects, + ignore_effects, option, + option_vars, + knob_turn_on_sampler) + + # TurnOnKnobAndBoilKettle + parameters = [gripper, knob, surface_to, kettle] + preconditions = { + LiftedAtom(TurnedOff, [knob]), + LiftedAtom(OnTop, [kettle, surface_to]), + LiftedAtom(KnobAndBurnerLinked, [knob, surface_to]) + } + add_effects = { + LiftedAtom(TurnedOn, [knob]), + LiftedAtom(KettleBoiling, [kettle, surface_to, knob]) + } + delete_effects = {LiftedAtom(TurnedOff, [knob])} + ignore_effects = { + AtPreTurnOn, AtPrePushOnTop, AtPreTurnOff, AtPrePullKettle + } + option = MoveAndTurnOnKnob + option_vars = [gripper, knob] + move_and_turn_on_knob_and_boil_kettle_nsrt = NSRT( + "MoveAndTurnOnKnobAndBoilKettle", parameters, preconditions, + add_effects, delete_effects, ignore_effects, option, option_vars, + move_and_knob_turn_on_sampler) # TurnOffKnob parameters = [gripper, knob] @@ -417,7 +507,6 @@ def knob_turn_off_sampler(state: State, goal: Set[GroundAtom], turn_off_knob_nsrt = NSRT("TurnOffKnob", parameters, preconditions, add_effects, delete_effects, ignore_effects, option, option_vars, knob_turn_off_sampler) - nsrts.add(turn_off_knob_nsrt) # PushOpenHingeDoor parameters = [gripper, hinge_door] @@ -459,7 +548,6 @@ def push_open_hinge_door_sampler(state: State, goal: Set[GroundAtom], delete_effects, ignore_effects, option, option_vars, push_open_hinge_door_sampler) - nsrts.add(push_open_hinge_door_nsrt) # PushCloseHingeDoor parameters = [gripper, hinge_door] @@ -499,6 +587,32 @@ def push_close_hinge_door_sampler(state: State, goal: Set[GroundAtom], delete_effects, ignore_effects, option, option_vars, push_close_hinge_door_sampler) + + # Add the relevant NSRTs to the set to be returned. + # NOTE: if kitchen_use_combo_move_nsrts is set to true, we use NSRTs + # that couple moving with other actions implicitly (i.e., move NSRTs + # aren't separate in any way). This is useful for e.g. in VLM predicate + # invention since moving places doesn't really turn on any predicates + # that are easily-classified. + if not CFG.kitchen_use_combo_move_nsrts: + nsrts.add(move_to_pre_push_on_top_nsrt) + nsrts.add(push_obj_on_obj_forward_nsrt) + nsrts.add(push_obj_on_obj_forward_and_boil_kettle_nsrt) + nsrts.add(turn_on_knob_nsrt) + nsrts.add(turn_on_knob_and_boil_kettle_nsrt) + else: + nsrts.add(push_kettle_onto_burner_nsrt) + nsrts.add(push_kettle_onto_burner_and_boil_nsrt) + nsrts.add(move_and_turn_on_knob_and_boil_kettle_nsrt) + nsrts.add(move_and_turn_on_knob_nsrt) + nsrts.add(move_to_pre_pull_kettle_nsrt) + nsrts.add(pull_kettle_nsrt) + nsrts.add(turn_off_switch_nsrt) + nsrts.add(turn_on_switch_nsrt) + nsrts.add(turn_off_knob_nsrt) + nsrts.add(push_open_hinge_door_nsrt) + nsrts.add(move_to_pre_turn_on_nsrt) + nsrts.add(move_to_pre_turn_off_nsrt) nsrts.add(push_close_hinge_door_nsrt) return nsrts diff --git a/predicators/ground_truth_models/kitchen/options.py b/predicators/ground_truth_models/kitchen/options.py index d1175f559c..ab0359b56a 100644 --- a/predicators/ground_truth_models/kitchen/options.py +++ b/predicators/ground_truth_models/kitchen/options.py @@ -143,7 +143,7 @@ def _MoveTo_terminal(state: State, memory: Dict, # Create copies just to preserve one-to-one-ness with NSRTs. for suffix in ["PreTurnOn", "PreTurnOff"]: - nsrt = ParameterizedOption( + opt = ParameterizedOption( f"MoveTo{suffix}", types=[gripper_type, on_off_type], # Parameter is a position to move to relative to the object. @@ -152,7 +152,7 @@ def _MoveTo_terminal(state: State, memory: Dict, initiable=_MoveTo_initiable, terminal=_MoveTo_terminal) - options.add(nsrt) + options.add(opt) # MoveToPrePushOnTop (different type) def _MoveToPrePushOnTop_initiable(state: State, memory: Dict, @@ -272,6 +272,47 @@ def _PushObjOnObjForward_terminal(state: State, memory: Dict, options.add(PushObjOnObjForward) + # PushKettleOntoBurner + def _PushKettleOntoBurner_initiable(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> bool: + gripper, obj, _ = objects + memory["gripper_infront_kettle"] = False + return _MoveTo_initiable(state, memory, [gripper, obj], params[:3]) + + def _PushKettleOntoBurner_policy(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> Action: + gripper, obj, _ = objects + if not memory["gripper_infront_kettle"]: + # Check if the MoveTo option has terminated. + if _MoveTo_terminal(state, memory, [gripper, obj], params[:3]): + memory["gripper_infront_kettle"] = True + else: + return _MoveTo_policy(state, memory, [gripper, obj], + params[:3]) + return _PushObjOnObjForward_policy(state, memory, objects, + params[3:]) + + def _PushKettleOntoBurner_terminal(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> bool: + del memory, params # unused + _, obj, obj2 = objects + return GroundAtom(OnTop, [obj, obj2]).holds(state) + + PushKettleOntoBurner = ParameterizedOption( + "PushKettleOntoBurner", + types=[gripper_type, kettle_type, surface_type], + # Parameter is an angle for pushing forward. + params_space=Box(np.array([-5.0, -5.0, -5.0, -np.pi]), + np.array([5.0, 5.0, 5.0, np.pi]), (4, )), + policy=_PushKettleOntoBurner_policy, + initiable=_PushKettleOntoBurner_initiable, + terminal=_PushKettleOntoBurner_terminal) + + options.add(PushKettleOntoBurner) + # PullKettle def _PullKettle_policy(state: State, memory: Dict, objects: Sequence[Object], @@ -408,6 +449,47 @@ def _TurnOnKnob_terminal(state: State, memory: Dict, terminal=_TurnOnKnob_terminal) options.add(TurnOnKnob) + # MoveAndTurnOnKnob + def _MoveAndTurnOnKnob_initiable(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> bool: + gripper, obj = objects + memory["gripper_infront_knob"] = False + return _MoveTo_initiable(state, memory, [gripper, obj], params[:3]) + + def _MoveAndTurnOnKnob_policy(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> Action: + gripper, obj = objects + if not memory["gripper_infront_knob"]: + # Check if the MoveTo option has terminated. + if _MoveTo_terminal(state, memory, [gripper, obj], params[:3]): + memory["gripper_infront_knob"] = True + else: + return _MoveTo_policy(state, memory, [gripper, obj], + params[:3]) + return _TurnOnKnob_policy(state, memory, objects, params[3:]) + + def _MoveAndTurnOnKnob_terminal(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> bool: + del memory, params # unused + _, obj = objects + # Use a more stringent threshold to avoid numerical issues. + return KitchenEnv.On_holds(state, [obj], + thresh_pad=cls.turn_knob_tol) + + MoveAndTurnOnKnob = ParameterizedOption( + "MoveAndTurnOnKnob", + types=[gripper_type, knob_type], + # The parameter is a push direction angle with respect to x. + params_space=Box(np.array([-5.0, -5.0, -5.0, -np.pi]), + np.array([5.0, 5.0, 5.0, np.pi]), (4, )), + policy=_MoveAndTurnOnKnob_policy, + initiable=_MoveAndTurnOnKnob_initiable, + terminal=_MoveAndTurnOnKnob_terminal) + options.add(MoveAndTurnOnKnob) + # TurnOffKnob def _TurnOffKnob_policy(state: State, memory: Dict, objects: Sequence[Object], diff --git a/predicators/settings.py b/predicators/settings.py index 0f3c31c884..9b5ad95d4f 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -323,6 +323,7 @@ class GlobalSettings: kitchen_use_perfect_samplers = False kitchen_goals = "all" kitchen_render_set_of_marks = False + kitchen_use_combo_move_nsrts = False # sticky table env parameters sticky_table_num_tables = 5 From 8dc76a562e94360728d8662198ca2d30ac77719c Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Thu, 23 May 2024 17:50:57 -0400 Subject: [PATCH 03/38] Image-option diff-based VLM prompting (#1656) * init commit * tested! * comments * yapf * linting --- .../datasets/generate_atom_trajs_with_vlm.py | 42 +++++++++++++++---- .../atom_labelling/img_option_diffs.txt | 3 ++ tests/datasets/test_datasets.py | 3 +- 3 files changed, 39 insertions(+), 9 deletions(-) create mode 100644 predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs.txt diff --git a/predicators/datasets/generate_atom_trajs_with_vlm.py b/predicators/datasets/generate_atom_trajs_with_vlm.py index aeae8eeedc..d342bec9ce 100644 --- a/predicators/datasets/generate_atom_trajs_with_vlm.py +++ b/predicators/datasets/generate_atom_trajs_with_vlm.py @@ -98,14 +98,40 @@ def _generate_prompt_for_scene_labelling( except FileNotFoundError: raise ValueError("Unknown VLM prompting option " + f"{CFG.grammar_search_vlm_atom_label_prompt_type}") - for atom_str in atoms_list: - prompt += f"\n{atom_str}" - for curr_imgs in traj.imgs: - # NOTE: we rip out just one img from each of the state - # images. This is fine/works for the case where we only - # have one camera view, but probably will need to be - # amended in the future! - ret_list.append((prompt, [curr_imgs[0]])) + if CFG.grammar_search_vlm_atom_label_prompt_type == "img_option_diffs": + # In this case, we need to load the 'per_scene_naive' prompt as well + # for the first timestep. + with open(filepath_prefix + "per_scene_naive.txt", + "r", + encoding="utf-8") as f: + init_prompt = f.read() + for atom_str in atoms_list: + init_prompt += f"\n{atom_str}" + ret_list.append((init_prompt, traj.imgs[0])) + # Now, we use actual difference-based prompting for the second timestep + # and beyond. + # The prompt ends with a section for 'Predicates', so list these. + for atom_str in atoms_list: + prompt += f"\n{atom_str}" + for i in range(1, len(traj.imgs)): + curr_prompt = prompt[:] + # NOTE: we rip out just one img from each of the state + # images. This is fine/works for the case where we only + # have one camera view, but probably will need to be + # amended in the future! + curr_prompt_imgs = [ + imgs_timestep[0] for imgs_timestep in traj.imgs[i - 1:i + 1] + ] + curr_prompt += "\n\nSkill executed between states: " + curr_prompt += traj.actions[i - 1].name + str( + traj.actions[i - 1].objects) + ret_list.append((curr_prompt, curr_prompt_imgs)) + else: + for atom_str in atoms_list: + prompt += f"\n{atom_str}" + for curr_imgs in traj.imgs: + # NOTE: same problem with ripping out images as in the above note. + ret_list.append((prompt, [curr_imgs[0]])) return ret_list diff --git a/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs.txt b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs.txt new file mode 100644 index 0000000000..4b293efe55 --- /dev/null +++ b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs.txt @@ -0,0 +1,3 @@ +You are a vision system for a robot. Your job is to output the values of the following predicates based on some information provided below. You will be provided with two images corresponding to the states before and after a particular skill is executed. You will also be provided with a list of predicates. Your job is to output the values of all the predicates in only the final scene. Pay careful attention to predicates that change between the two scenes, and list these separately at the end of your response. Note that in some scenes, there might be no changes. Output each predicate value as a bulleted list with each predicate and value on a different line. For each output value, provide an explanation as to why you labelled this predicate as having this particular value. Use the format: : . . + +Predicates to label in scenes: \ No newline at end of file diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py index e2118d6add..29c08d831c 100644 --- a/tests/datasets/test_datasets.py +++ b/tests/datasets/test_datasets.py @@ -592,7 +592,8 @@ def test_loading_saved_vlm_img_demos_folder_non_dummy_goal(): ("options_labels_whole_traj", "per_scene_naive"), ("naive_whole_traj", "per_scene_cot"), ("not_a_real_prompt_type", "per_scene_cot"), - ("naive_whole_traj", "not_a_real_prompt_type")]) + ("naive_whole_traj", "not_a_real_prompt_type"), + ("options_labels_whole_traj", "img_option_diffs")]) def test_loading_saved_vlm_img_demos_folder_dummy_goal( atom_proposal_prompt_type, atom_labelling_prompt_type): """Test loading a dataset from img demo files.""" From 6c78615af0137d84f05627690eb88066c1142936 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Mon, 27 May 2024 12:12:48 -0400 Subject: [PATCH 04/38] good to merge (#1657) --- predicators/envs/kitchen.py | 10 +++++----- predicators/ground_truth_models/kitchen/options.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/predicators/envs/kitchen.py b/predicators/envs/kitchen.py index 20ca492405..d4d9be3471 100644 --- a/predicators/envs/kitchen.py +++ b/predicators/envs/kitchen.py @@ -88,7 +88,7 @@ class KitchenEnv(BaseEnv): obj_name_to_pre_push_dpos = { ("kettle", "on"): (-0.05, -0.3, -0.12), ("kettle", "off"): (0.0, 0.0, 0.08), - ("knob4", "on"): (-0.1, -0.15, 0.05), + ("knob4", "on"): (-0.1, -0.10, 0.05), ("knob4", "off"): (0.05, -0.12, -0.05), ("light", "on"): (0.1, -0.05, -0.05), ("light", "off"): (-0.1, -0.05, -0.05), @@ -195,13 +195,13 @@ def render(self, font = utils.get_scaled_default_font(draw, 3) # Define the text and position burner1_text = "burner1" - burner1_position = (300, 290) + burner1_position = (300, 305) burner2_text = "burner2" - burner2_position = (210, 310) + burner2_position = (210, 325) burner3_text = "burner3" - burner3_position = (260, 235) + burner3_position = (260, 250) burner4_text = "burner4" - burner4_position = (185, 245) + burner4_position = (185, 260) burner1_img = utils.add_text_to_draw_img(draw, burner1_position, burner1_text, font) burner2_img = utils.add_text_to_draw_img(burner1_img, diff --git a/predicators/ground_truth_models/kitchen/options.py b/predicators/ground_truth_models/kitchen/options.py index ab0359b56a..9eefbb2a33 100644 --- a/predicators/ground_truth_models/kitchen/options.py +++ b/predicators/ground_truth_models/kitchen/options.py @@ -30,7 +30,7 @@ class KitchenGroundTruthOptionFactory(GroundTruthOptionFactory): # Keep pushing a bit even if the On classifier holds. push_lr_thresh_pad: ClassVar[float] = 0.02 push_microhandle_thresh_pad: ClassVar[float] = 0.02 - turn_knob_tol: ClassVar[float] = 0.01 # for twisting the knob + turn_knob_tol: ClassVar[float] = -0.03 # for twisting the knob @classmethod def get_env_names(cls) -> Set[str]: From ca4555d1b2d497a5c2e9b126d8f4ef2cc7b9df19 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Mon, 27 May 2024 14:22:41 -0400 Subject: [PATCH 05/38] Improve vlm predicate parsing (#1658) * ready to go! * implements improved vlm prompting and vastly-simplified parsing --- .gitignore | 1 + .../datasets/generate_atom_trajs_with_vlm.py | 114 ++++++++---------- .../options_labels_whole_traj.txt | 2 +- predicators/structs.py | 7 -- tests/datasets/test_datasets.py | 2 +- 5 files changed, 50 insertions(+), 76 deletions(-) diff --git a/.gitignore b/.gitignore index 0da004bdcb..9439aec1a5 100644 --- a/.gitignore +++ b/.gitignore @@ -21,6 +21,7 @@ saved_approaches saved_datasets scripts/results pretrained_model_cache +tests/datasets/mock_vlm_datasets/cache/ machines.txt *_vision_data tests/_fake_trajs diff --git a/predicators/datasets/generate_atom_trajs_with_vlm.py b/predicators/datasets/generate_atom_trajs_with_vlm.py index d342bec9ce..5880630166 100644 --- a/predicators/datasets/generate_atom_trajs_with_vlm.py +++ b/predicators/datasets/generate_atom_trajs_with_vlm.py @@ -341,20 +341,11 @@ def _parse_structured_state_into_ground_atoms( assert goal_predicate.name == "DummyGoal" use_dummy_goal = True - # We also check whether there is precisely one "object" type that is - # a superset of all other object types. - obj_type = None - for t in env.types: - obj_type = t.oldest_ancestor - if obj_type.name != "object": - obj_type = None - break - def _get_vlm_query_str(pred_name: str, objects: Sequence[Object]) -> str: return pred_name + "(" + ", ".join( str(obj.name) for obj in objects) + ")" # pragma: no cover - pred_name_to_pred = {} + pred_name_and_obj_types_to_pred = {} atoms_trajs = [] # Loop through all trajectories in the structured_state_trajs and convert # each one to a sequence of sets of GroundAtoms. @@ -378,6 +369,8 @@ def _get_vlm_query_str(pred_name: str, objects: Sequence[Object]) -> str: # reached or not. assert DUMMY_GOAL_OBJ_NAME in curr_obj_name_to_obj + # Now, start converting each structured state into a set of ground + # atoms. for j, structured_state in enumerate(traj): curr_ground_atoms_state = set() @@ -387,67 +380,38 @@ def _get_vlm_query_str(pred_name: str, objects: Sequence[Object]) -> str: state_trajs[i][j], known_predicates) for pred_name, objs_and_val_dict in structured_state.items(): - # IMPORTANT NOTE: this currently assumes that the data is such - # that a predicate with a certain name (e.g. "Sliced") - # always appears with the same number of object arguments - # (e.g. Sliced(apple), and never - # Sliced(apple, cutting_tool)). We might want to explicitly - # check for this in the future. - if pred_name not in pred_name_to_pred: - if len(objs_and_val_dict.keys()) == 1: - # NOTE: this below code doesn't do the right thing - # when there are multiple of the predicate that - # are true with different objects of the same type - # (e.g. Covers(obj1, targ1) and Covers(obj2, targ2)). - # We might want to do something about this. - # In this case, we make a predicate that takes in - # exactly one types argument. - for obj_args in objs_and_val_dict.keys(): - # We need to construct the types being - # fed into this predicate. - pred_types = [] - for obj_name in obj_args: - curr_obj = curr_obj_name_to_obj[obj_name] - pred_types.append(curr_obj.type) - pred_name_to_pred[ - pred_name] = utils.create_vlm_predicate( - pred_name, pred_types, + for pred_i, (objs_strs, truth_val) in enumerate( + sorted(objs_and_val_dict.items())): + objs_types = [ + curr_obj_name_to_obj[obj_name].type + for obj_name in objs_strs + ] + pred_name_and_obj_types_str = pred_name + "(" + ",".join( + str(obj_type.name) for obj_type in objs_types) + ")" + if pred_name_and_obj_types_str not in \ + pred_name_and_obj_types_to_pred: + # NOTE: we use 'pred_i' here as a unique index such + # that different predicates with the same name but + # different object types (e.g. On(light) vs. + # On(kettle, burner)) have a different name so that + # the planner doesn't complain about this during + # planning. + pred_name_and_obj_types_to_pred[ + pred_name_and_obj_types_str] = \ + utils.create_vlm_predicate(pred_name + + str(pred_i), objs_types, partial(_get_vlm_query_str, pred_name)) - else: - # In this case, we need to make a predicate that - # takes in the generic 'object' type such that - # multiple different objs could potentially be - # subbed in. - # Start by checking that the number of object - # args are always the same - num_args = 0 - for obj_args in objs_and_val_dict.keys(): - if num_args == 0: - num_args = len(obj_args) - else: - assert num_args == len(obj_args) - # Given this, add one new predicate with num_args - # number of 'object' type arguments. - assert obj_type is not None, ( - "VLM atom parsing " - "failure; please add an 'object' type to your " - "environment that is a supertype of all other " - "types.") - pred_name_to_pred[ - pred_name] = utils.create_vlm_predicate( - pred_name, [obj_type for _ in range(num_args)], - partial(_get_vlm_query_str, pred_name)) - - # Given that we've now built up predicates and object - # dictionaries. We can now convert the current state into - # ground atoms! - for obj_args, truth_value in objs_and_val_dict.items(): - if truth_value: + # Given that we've now built up predicates and object + # dictionaries. We can now convert the current state into + # ground atoms! + if truth_val: curr_ground_atoms_state.add( GroundAtom( - pred_name_to_pred[pred_name], - [curr_obj_name_to_obj[o] for o in obj_args])) + pred_name_and_obj_types_to_pred[ + pred_name_and_obj_types_str], + [curr_obj_name_to_obj[o] for o in objs_strs])) curr_atoms_traj.append(curr_ground_atoms_state) + # Bookkeeping for the goal(s). if assume_goal_holds_at_end: curr_atoms_traj[-1] |= train_tasks[i].goal atoms_trajs.append(curr_atoms_traj) @@ -594,6 +558,10 @@ def _parse_atoms_txt_into_structured_state( pattern_predicate, state_block_match) current_predicate_data: Dict[str, Dict[Tuple[str, ...], bool]] = {} for predicate_match in predicate_matches_within_state_block: + # Skip evaluating any predicate values that aren't explicitly + # labelled as one of the below. + if predicate_match[2] not in ["True", "False", "Unknown"]: + continue classifier_name = predicate_match[0] objects = tuple(map(str.strip, predicate_match[1].split(','))) truth_value = predicate_match[2] == 'True' @@ -750,9 +718,21 @@ def create_ground_atom_data_from_generated_demos( segments = _segment_with_option_changes(traj, set(), None) curr_traj_states_for_vlm: List[State] = [] curr_traj_actions_for_vlm: List[Action] = [] + total_num_segment_states = 0 + first_iteration = True for segment in segments: curr_traj_states_for_vlm.append(segment.states[0]) curr_traj_actions_for_vlm.append(segment.actions[0]) + total_num_segment_states += len(segment.states) + if first_iteration: + first_iteration = False + else: + total_num_segment_states -= 1 # avoid double-counting states! + if total_num_segment_states != len(traj.states): # pragma: no cover. + logging.info( + ("WARNING: there are fewer total states after option-based " + "segmentation than there are in the original trajectory. " + "Likely there is an issue with segmentation!")) # We manually add the final two states (initial state and terminal # state of the final option). curr_traj_states_for_vlm.append(traj.states[-1]) diff --git a/predicators/datasets/vlm_input_data_prompts/atom_proposal/options_labels_whole_traj.txt b/predicators/datasets/vlm_input_data_prompts/atom_proposal/options_labels_whole_traj.txt index 0dddfe7001..2b6af35bed 100644 --- a/predicators/datasets/vlm_input_data_prompts/atom_proposal/options_labels_whole_traj.txt +++ b/predicators/datasets/vlm_input_data_prompts/atom_proposal/options_labels_whole_traj.txt @@ -1 +1 @@ -You are a robotic vision system whose job is to output a structured set of predicates useful for describing important concepts in the following demonstration of a task. You will be provided with a list of actions used during the task, as well as images of states before and after every action execution. Please provide predicates in terms of the following objects: {objs}. For each predicate, output it in the following format: predicate_name(obj1, obj2, obj3...). Start by generating predicates that change before and after each action. After this, generate any other predicates that perhaps do not change but are still important to describing the demonstration shown. For each predicate you generate, also generate predicates that are synonyms and antonyms so that any predicate that is even tangentially relevant to the demonstrations is generated. \ No newline at end of file +You are a robotic vision system whose job is to output a structured set of predicates useful for describing important concepts in the following demonstration of a task. You will be provided with a list of actions used during the task, as well as images of states before and after every action execution. Please provide predicates in terms of the following objects: {objs}. For each predicate, output it in the following format: predicate_name(obj1, obj2, obj3...). Start by generating predicates that change before and after each action. After this, generate any other predicates that perhaps do not change but are still important to describing the demonstration shown. For each predicate you generate, also generate some predicates that are synonyms and antonyms so that any predicate that is even tangentially relevant to the demonstrations is generated. \ No newline at end of file diff --git a/predicators/structs.py b/predicators/structs.py index d6bc7163d7..889905793d 100644 --- a/predicators/structs.py +++ b/predicators/structs.py @@ -31,13 +31,6 @@ def dim(self) -> int: """Dimensionality of the feature vector of this object type.""" return len(self.feature_names) - @property - def oldest_ancestor(self) -> Type: - """Crawl up all the parent types to return the one at the top.""" - if self.parent is None: - return self - return self.parent.oldest_ancestor - def __call__(self, name: str) -> _TypedEntity: """Convenience method for generating _TypedEntities.""" if name.startswith("?"): diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py index 29c08d831c..520ddfd274 100644 --- a/tests/datasets/test_datasets.py +++ b/tests/datasets/test_datasets.py @@ -628,7 +628,7 @@ def test_loading_saved_vlm_img_demos_folder_dummy_goal( assert len(loaded_dataset.trajectories) == 1 assert len(loaded_dataset.annotations) == 1 assert len(loaded_dataset.annotations[0][0]) == 1 - assert "Holding(spoon:spoon)" in str(loaded_dataset.annotations[0][0]) + assert "Holding0(spoon:spoon)" in str(loaded_dataset.annotations[0][0]) assert "DummyGoal" in str(loaded_dataset.annotations[0][-1]) else: with pytest.raises(ValueError) as e: From 5aef831ee6b66ca7ed30c71722218af762532716 Mon Sep 17 00:00:00 2001 From: Alicia Li <102700848+matrixbalto@users.noreply.github.com> Date: Wed, 29 May 2024 12:05:53 -0400 Subject: [PATCH 06/38] Added GridRowDoor environment! (#1659) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * pr grid_row_door * minor changes * minor fixes * minor minor fixes --------- Co-authored-by: “matrixbalto” <“ilfangliu@gmail.com”> --- predicators/envs/grid_row.py | 104 +++++++++++++ .../ground_truth_models/grid_row/nsrts.py | 140 ++++++++++++++++++ .../ground_truth_models/grid_row/options.py | 74 +++++++++ tests/envs/test_grid_row.py | 113 +++++++++++++- 4 files changed, 430 insertions(+), 1 deletion(-) diff --git a/predicators/envs/grid_row.py b/predicators/envs/grid_row.py index 04f63019ac..11ac2214a8 100644 --- a/predicators/envs/grid_row.py +++ b/predicators/envs/grid_row.py @@ -174,3 +174,107 @@ def _Adjacent_holds(self, state: State, objects: Sequence[Object]) -> bool: # return abs(dist - 1.0) < 1e-3 del state # not used return obj1 in self._cell_to_neighbors[obj2] + + +class GridRowDoorEnv(GridRowEnv): + """Simple variant on GridRow where there is also a door.""" + + def __init__(self, use_gui: bool = True) -> None: + super().__init__(use_gui) + # type door with features ['x', and 'open'] + self._door_type = Type("door", ["x", 'open']) + self._door = Object("door", self._door_type) + + self._DoorInCell = Predicate("DoorInCell", + [self._door_type, self._cell_type], + self._In_holds) + + @classmethod + def get_name(cls) -> str: + return "grid_row_door" + + @property + def predicates(self) -> Set[Predicate]: + return { + self._RobotInCell, self._LightInCell, self._LightOn, + self._LightOff, self._Adjacent, self._DoorInCell + } + + @property + def types(self) -> Set[Type]: + return { + self._robot_type, self._cell_type, self._light_type, + self._door_type + } + + @property + def action_space(self) -> Box: + # dx, dlight, ddoor + return Box(-np.inf, np.inf, (3, )) + + def _get_tasks(self, num: int, + rng: np.random.Generator) -> List[EnvironmentTask]: + # There is only one goal in this environment: to turn the light on. + goal = {GroundAtom(self._LightOn, [self._light])} + # The only variation in the initial state is the light target level. + tasks: List[EnvironmentTask] = [] + while len(tasks) < num: + state_dict = { + self._robot: { + "x": 0.5, + }, + self._light: { + "x": len(self._cells) - 0.5, + "level": 0.0, + "target": rng.uniform(0.5, 1.0), + }, + self._door: { + "x": rng.choice(range(2, len(self._cells))) - 0.5, + "open": 0.0 + } + } + for i, cell in enumerate(self._cells): + state_dict[cell] = {"x": i + 0.5} + state = utils.create_state_from_dict(state_dict) + tasks.append(EnvironmentTask(state, goal)) + return tasks + + def simulate(self, state: State, action: Action) -> State: + assert self.action_space.contains(action.arr) + next_state = state.copy() + dx, dlight, ddoor = action.arr + door_pos = state.get(self._door, "x") + robbot_pos = state.get(self._robot, "x") + door_open = state.get(self._door, "open") + robot_cells = [ + c for c in self._cells if self._In_holds(state, [self._robot, c]) + ] + door_cells = [ + c for c in self._cells if self._In_holds(state, [self._door, c]) + ] + assert len(door_cells) == 1 + # Apply ddoor if we're in same cell as door + door_cell = door_cells[0] + robot_cell = robot_cells[0] + if robot_cell == door_cell and not door_open and ddoor == 1.0: + next_state.set(self._door, "open", 1.0) + # Apply dlight if we're in the same cell as the light. + assert len(robot_cells) == 1 + light_cells = [ + c for c in self._cells if self._In_holds(state, [self._light, c]) + ] + assert len(light_cells) == 1 + light_cell = light_cells[0] + if robot_cell == light_cell: + new_light_level = np.clip( + state.get(self._light, "level") + dlight, 0.0, 1.0) + next_state.set(self._light, "level", new_light_level) + + if door_open == 1.0 or (robbot_pos <= door_pos + and robbot_pos + dx <= door_pos): + # Apply dx to robot. + new_x = np.clip( + state.get(self._robot, "x") + dx, 0.0, len(self._cells)) + next_state.set(self._robot, "x", new_x) + + return next_state diff --git a/predicators/ground_truth_models/grid_row/nsrts.py b/predicators/ground_truth_models/grid_row/nsrts.py index 4f74175341..fe476b49ac 100644 --- a/predicators/ground_truth_models/grid_row/nsrts.py +++ b/predicators/ground_truth_models/grid_row/nsrts.py @@ -147,3 +147,143 @@ def light_sampler(state: State, goal: Set[GroundAtom], nsrts.add(impossible_nsrt) return nsrts + + +class GridRowDoorGroundTruthNSRTFactory(GridRowGroundTruthNSRTFactory): + """Ground-truth NSRTs for the grid row door environment.""" + + @classmethod + def get_env_names(cls) -> Set[str]: + return {"grid_row_door"} + + @staticmethod + def get_nsrts(env_name: str, types: Dict[str, Type], + predicates: Dict[str, Predicate], + options: Dict[str, ParameterizedOption]) -> Set[NSRT]: + # Types + robot_type = types["robot"] + light_type = types["light"] + cell_type = types["cell"] + door_type = types["door"] + + # Predicates + RobotInCell = predicates["RobotInCell"] + LightInCell = predicates["LightInCell"] + LightOn = predicates["LightOn"] + LightOff = predicates["LightOff"] + Adjacent = predicates["Adjacent"] + DoorInCell = predicates["DoorInCell"] + + # Options + MoveRobot = options["MoveRobot"] + TurnOnLight = options["TurnOnLight"] + TurnOffLight = options["TurnOffLight"] + OpenDoor = options["OpenDoor"] + + nsrts = set() + + def light_sampler(state: State, goal: Set[GroundAtom], + rng: np.random.Generator, + objs: Sequence[Object]) -> Array: + del state, goal, objs # unused + # Note: just return a random value from -1 to 1 + return np.array([rng.uniform(-1.0, 1.0)], dtype=np.float32) + + # MoveRobot + robot = Variable("?robot", robot_type) + current_cell = Variable("?current_cell", cell_type) + target_cell = Variable("?target_cell", cell_type) + parameters = [robot, current_cell, target_cell] + option_vars = parameters + option = MoveRobot + preconditions = { + LiftedAtom(Adjacent, [current_cell, target_cell]), + LiftedAtom(RobotInCell, [robot, current_cell]), + } + add_effects = { + LiftedAtom(RobotInCell, [robot, target_cell]), + } + delete_effects = { + LiftedAtom(RobotInCell, [robot, current_cell]), + } + ignore_effects: Set[Predicate] = set() + move_robot_nsrt = NSRT("MoveRobot", parameters, preconditions, + add_effects, delete_effects, ignore_effects, + option, option_vars, null_sampler) + nsrts.add(move_robot_nsrt) + + # TurnOnLight + robot = Variable("?robot", robot_type) + current_cell = Variable("?current_cell", cell_type) + light = Variable("?light", light_type) + parameters = [robot, current_cell, light] + option_vars = parameters + option = TurnOnLight + preconditions = { + LiftedAtom(LightInCell, [light, current_cell]), + LiftedAtom(RobotInCell, [robot, current_cell]), + LiftedAtom(LightOff, [light]), + } + add_effects = { + LiftedAtom(LightOn, [light]), + } + delete_effects = { + LiftedAtom(LightOff, [light]), + } + ignore_effects = set() + turn_light_on_nsrt = NSRT("TurnOnLight", parameters, preconditions, + add_effects, delete_effects, ignore_effects, + option, option_vars, light_sampler) + nsrts.add(turn_light_on_nsrt) + + # TurnOffLight + robot = Variable("?robot", robot_type) + current_cell = Variable("?current_cell", cell_type) + light = Variable("?light", light_type) + parameters = [robot, current_cell, light] + option_vars = parameters + option = TurnOffLight + preconditions = { + LiftedAtom(LightInCell, [light, current_cell]), + LiftedAtom(RobotInCell, [robot, current_cell]), + LiftedAtom(LightOn, [light]), + } + add_effects = { + LiftedAtom(LightOff, [light]), + } + delete_effects = { + LiftedAtom(LightOn, [light]), + } + ignore_effects = set() + turn_light_off_nsrt = NSRT("TurnOffLight", parameters, preconditions, + add_effects, delete_effects, ignore_effects, + option, option_vars, light_sampler) + nsrts.add(turn_light_off_nsrt) + + # OpenDoor + def door_sampler(state: State, goal: Set[GroundAtom], + rng: np.random.Generator, + objs: Sequence[Object]) -> Array: + del state, goal, objs # unused + # Note: return 1.0 to show door is now open + return np.array([rng.uniform(1.0, 1.0)], dtype=np.float32) + + robot = Variable("?robot", robot_type) + current_cell = Variable("?current_cell", cell_type) + door = Variable("?door", door_type) + parameters = [robot, current_cell, door] + option_vars = parameters + option = OpenDoor + preconditions = { + LiftedAtom(DoorInCell, [door, current_cell]), + LiftedAtom(RobotInCell, [robot, current_cell]), + } + add_effects = set() + delete_effects = set() + ignore_effects = set() + open_door_nsrt = NSRT("OpenDoor", parameters, preconditions, + add_effects, delete_effects, ignore_effects, + option, option_vars, door_sampler) + nsrts.add(open_door_nsrt) + + return nsrts diff --git a/predicators/ground_truth_models/grid_row/options.py b/predicators/ground_truth_models/grid_row/options.py index 1068f9f927..ce513b5665 100644 --- a/predicators/ground_truth_models/grid_row/options.py +++ b/predicators/ground_truth_models/grid_row/options.py @@ -80,3 +80,77 @@ def _null_policy(state: State, memory: Dict, objects: Sequence[Object], ) return {MoveRobot, TurnOnLight, TurnOffLight, JumpToLight} + + +class GridRowDoorGroundTruthOptionFactory(GridRowGroundTruthOptionFactory): + """Ground-truth options for the grid row door environment.""" + + @classmethod + def get_env_names(cls) -> Set[str]: + return {"grid_row_door"} + + @classmethod + def get_options(cls, env_name: str, types: Dict[str, Type], + predicates: Dict[str, Predicate], + action_space: Box) -> Set[ParameterizedOption]: + + robot_type = types["robot"] + light_type = types["light"] + cell_type = types["cell"] + door_type = types["door"] + + # MoveTo + def _MoveTo_policy(state: State, memory: Dict, + objects: Sequence[Object], params: Array) -> Action: + del memory, params # unused + robot, _, target_cell = objects + rob_x = state.get(robot, "x") + target_x = state.get(target_cell, "x") + dx = target_x - rob_x + return Action(np.array([dx, 0.0, 0.0], dtype=np.float32)) + + MoveRobot = utils.SingletonParameterizedOption( + "MoveRobot", + types=[robot_type, cell_type, cell_type], + policy=_MoveTo_policy, + ) + + def _toggle_light_policy(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> Action: + del state, objects, memory # unused + dlight, = params + return Action(np.array([0.0, dlight, 0.0], dtype=np.float32)) + + # TurnOnLight + TurnOnLight = utils.SingletonParameterizedOption( + "TurnOnLight", + types=[robot_type, cell_type, light_type], + policy=_toggle_light_policy, + params_space=Box(-1.0, 1.0, (1, )), + ) + + # TurnOffLight + TurnOffLight = utils.SingletonParameterizedOption( + "TurnOffLight", + types=[robot_type, cell_type, light_type], + policy=_toggle_light_policy, + params_space=Box(-1.0, 1.0, (1, )), + ) + + # OpenDoor + def _toggle_door_policy(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> Action: + del state, objects, memory # unused + ddoor, = params + return Action(np.array([0.0, 0.0, ddoor], dtype=np.float32)) + + OpenDoor = utils.SingletonParameterizedOption( + "OpenDoor", + types=[robot_type, cell_type, door_type], + policy=_toggle_door_policy, + params_space=Box(0.0, 1.0, (1, )), + ) + + return {MoveRobot, TurnOnLight, TurnOffLight, OpenDoor} diff --git a/tests/envs/test_grid_row.py b/tests/envs/test_grid_row.py index d39b7f31b9..19b1214a67 100644 --- a/tests/envs/test_grid_row.py +++ b/tests/envs/test_grid_row.py @@ -4,7 +4,7 @@ import pytest from predicators import utils -from predicators.envs.grid_row import GridRowEnv +from predicators.envs.grid_row import GridRowDoorEnv, GridRowEnv from predicators.ground_truth_models import get_gt_nsrts, get_gt_options @@ -134,3 +134,114 @@ def test_grid_row(): state = env.simulate(state, action) assert option.terminal(state) assert not all(a.holds(state) for a in ground_nsrt.add_effects) + + +def test_grid_row_door(): + """Tests for the Grid Row Door environment.""" + utils.reset_config({ + "env": "grid_row_door", + "num_train_tasks": 1, + "num_test_tasks": 2, + "grid_row_num_cells": 10, + }) + env = GridRowDoorEnv() + assert env.get_name() == "grid_row_door" + for env_task in env.get_train_tasks(): + task = env_task.task + for obj in task.init: + assert len(obj.type.feature_names) == len(task.init[obj]) + for env_task in env.get_test_tasks(): + task = env_task.task + for obj in task.init: + assert len(obj.type.feature_names) == len(task.init[obj]) + assert len(env.predicates) == 6 + Adjacent, DoorInCell, LightInCell, LightOff, LightOn, RobotInCell = \ + sorted(env.predicates) + assert Adjacent.name == "Adjacent" + assert LightInCell.name == "LightInCell" + assert LightOff.name == "LightOff" + assert LightOn.name == "LightOn" + assert RobotInCell.name == "RobotInCell" + assert DoorInCell.name == "DoorInCell" + assert env.goal_predicates == {LightOn} + options = get_gt_options(env.get_name()) + assert len(env.types) == 4 + cell_type, door_type, light_type, robot_type = sorted(env.types) + assert cell_type.name == "cell" + assert light_type.name == "light" + assert robot_type.name == "robot" + assert door_type.name == "door" + assert env.action_space.shape == (3, ) + nsrts = get_gt_nsrts(env.get_name(), env.predicates, options) + assert len(nsrts) == 4 + assert len(options) == len(nsrts) + env_train_tasks = env.get_train_tasks() + assert len(env_train_tasks) == 1 + env_test_tasks = env.get_test_tasks() + assert len(env_test_tasks) == 2 + env_task = env_test_tasks[1] + env.reset("test", 1) + with pytest.raises(NotImplementedError): + env.render(caption="Test") + + # Test NSRTs. + MoveRobot, OpenDoor, TurnOffLight, TurnOnLight = sorted(nsrts) + assert OpenDoor.name == "OpenDoor" + assert MoveRobot.name == "MoveRobot" + assert TurnOffLight.name == "TurnOffLight" + assert TurnOnLight.name == "TurnOnLight" + + init_state = env_test_tasks[0].task.init + rng = np.random.default_rng(123) + + # Test successful turning on the light. + robot, = init_state.get_objects(robot_type) + light, = init_state.get_objects(light_type) + door, = init_state.get_objects(door_type) + cell_order = sorted(init_state.get_objects(cell_type), + key=lambda o: int(o.name[len("cell"):])) + ground_nsrt_plan = [] + # First move to the light. + state = init_state.copy() + for cell, next_cell in zip(cell_order[:-1], cell_order[1:]): + if len(ground_nsrt_plan) == state.get(door, "x") - 0.5: + ground_nsrt_plan.append(OpenDoor.ground([robot, cell, door])) + ground_nsrt_plan.append(MoveRobot.ground([robot, cell, next_cell])) + else: + ground_nsrt_plan.append(MoveRobot.ground([robot, cell, next_cell])) + rng = np.random.default_rng(123) + for ground_nsrt in ground_nsrt_plan: + assert all(a.holds(state) for a in ground_nsrt.preconditions) + option = ground_nsrt.sample_option(state, set(), rng) + assert option.initiable(state) + action = option.policy(state) + state = env.simulate(state, action) + assert option.terminal(state) + assert all(a.holds(state) for a in ground_nsrt.add_effects) + assert not any(a.holds(state) for a in ground_nsrt.delete_effects) + # Now repeatedly turn on the light until it succeeds. + ground_nsrt = TurnOnLight.ground([robot, cell_order[-1], light]) + for _ in range(100): + assert all(a.holds(state) for a in ground_nsrt.preconditions) + option = ground_nsrt.sample_option(state, set(), rng) + assert option.initiable(state) + action = option.policy(state) + state = env.simulate(state, action) + assert option.terminal(state) + if all(a.holds(state) for a in ground_nsrt.add_effects): + break + assert all(a.holds(state) for a in ground_nsrt.add_effects) + assert not any(a.holds(state) for a in ground_nsrt.delete_effects) + # Now repeatedly turn off the light until it succeeds. + ground_nsrt = TurnOffLight.ground([robot, cell_order[-1], light]) + for _ in range(100): + assert all(a.holds(state) for a in ground_nsrt.preconditions) + option = ground_nsrt.sample_option(state, set(), rng) + assert option.initiable(state) + action = option.policy(state) + state = env.simulate(state, action) + assert option.terminal(state) + if all(a.holds(state) for a in ground_nsrt.add_effects): + break + assert all(a.holds(state) for a in ground_nsrt.add_effects) + assert not any(a.holds(state) for a in ground_nsrt.delete_effects) From 188d51efa48f8288d437fc73175b0612d741fbd9 Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Thu, 30 May 2024 11:51:16 -0400 Subject: [PATCH 07/38] Support VLM predicate invention in BurgerEnv (#1660) * Initial commit. Just try to get the pipeline working with all predicates given. * Debugging. * Get image rendering to work properly so that VLM can take the image in as a valid input. * Get pipeline to run. * Remove change that made pipeline work with python < 3.10. * Uncomment caching model completions and hash the image ids to shorten the length of the cacheing directory. * Update comment. * Get pylint and mypy tests to pass. * Run autoformatter. * Fix more tests. * Update comment. * fixes kitchen * Address PR comments. * Fix test_abstract(). * Fix test_create_pddl(). --------- Co-authored-by: Nishanth Kumar --- .../datasets/generate_atom_trajs_with_vlm.py | 13 +- predicators/envs/burger.py | 143 +++++++++++++----- predicators/envs/kitchen.py | 1 + predicators/perception/kitchen_perceiver.py | 3 +- predicators/pretrained_model_interface.py | 10 +- predicators/utils.py | 4 +- tests/datasets/test_datasets.py | 3 +- tests/envs/test_burger.py | 2 +- tests/test_utils.py | 4 +- 9 files changed, 132 insertions(+), 51 deletions(-) diff --git a/predicators/datasets/generate_atom_trajs_with_vlm.py b/predicators/datasets/generate_atom_trajs_with_vlm.py index 5880630166..e1aee674aa 100644 --- a/predicators/datasets/generate_atom_trajs_with_vlm.py +++ b/predicators/datasets/generate_atom_trajs_with_vlm.py @@ -733,19 +733,18 @@ def create_ground_atom_data_from_generated_demos( ("WARNING: there are fewer total states after option-based " "segmentation than there are in the original trajectory. " "Likely there is an issue with segmentation!")) - # We manually add the final two states (initial state and terminal - # state of the final option). + # We manually add the final state of the final option. curr_traj_states_for_vlm.append(traj.states[-1]) # Pull out the images within the states we've saved for the trajectory. - # We assume that images are saved in the state's simulator_state - # field. + # We assume that the state's simulator_state attribute is a dictionary, + # and that the images for each state are accessed by the key "images". state_imgs: List[List[PIL.Image.Image]] = [] for state in curr_traj_states_for_vlm: - assert isinstance(state.simulator_state, List) - assert len(state.simulator_state) > 0 + assert state.simulator_state is not None + assert "images" in state.simulator_state state_imgs.append([ PIL.Image.fromarray(img_arr) # type: ignore - for img_arr in state.simulator_state + for img_arr in state.simulator_state["images"] ]) img_option_trajs.append( ImageOptionTrajectory( diff --git a/predicators/envs/burger.py b/predicators/envs/burger.py index fb64f707e6..8c3581e977 100644 --- a/predicators/envs/burger.py +++ b/predicators/envs/burger.py @@ -5,21 +5,24 @@ Nicole Thean (https://github.com/nicolethean). """ +import copy +import io import logging from typing import Callable, List, Optional, Sequence, Set, Tuple import matplotlib import matplotlib.image as mpimg import matplotlib.pyplot as plt -# import pygame import numpy as np from gym.spaces import Box +from PIL import Image from predicators import utils from predicators.envs import BaseEnv from predicators.settings import CFG -from predicators.structs import Action, EnvironmentTask, GroundAtom, Object, \ - Predicate, State, Type +from predicators.structs import Action, DefaultEnvironmentTask, \ + EnvironmentTask, GroundAtom, Object, Observation, Predicate, State, Type, \ + Video class BurgerEnv(BaseEnv): @@ -56,7 +59,8 @@ class BurgerEnv(BaseEnv): _item_type = Type("item", [], _object_type) _station_type = Type("station", [], _object_type) - _robot_type = Type("robot", ["row", "col", "fingers", "dir"]) + _robot_type = Type("robot", ["row", "col", "z", "fingers", "dir"], + _object_type) _patty_type = Type("patty", ["row", "col", "z"], _item_type) _tomato_type = Type("tomato", ["row", "col", "z"], _item_type) @@ -111,7 +115,7 @@ def __init__(self, use_gui: bool = True) -> None: ], self._GoalHack_holds) # Static objects (exist no matter the settings) - self._robot = Object("robby", self._robot_type) + self._robot = Object("robot", self._robot_type) self._grill = Object("grill", self._grill_type) self._cutting_board = Object("cutting_board", self._cutting_board_type) @@ -132,13 +136,14 @@ def _get_tasks(self, num: int, rng: np.random.Generator) -> List[EnvironmentTask]: del rng # unused tasks = [] - - # Add robot, grill, and cutting board state_dict = {} hidden_state = {} + + # Add robot, grill, and cutting board state_dict[self._robot] = { "row": 2, "col": 2, + "z": 0, "fingers": 0.0, "dir": 3 } @@ -177,13 +182,20 @@ def _get_tasks(self, num: int, GroundAtom(self._On, [top_bun, tomato]), GroundAtom(self._IsCooked, [patty]), GroundAtom(self._IsSliced, [tomato]), + # GroundAtom(self._GoalHack, [bottom_bun, patty, cheese, tomato, + # top_bun]) } for _ in range(num): state = utils.create_state_from_dict(state_dict) - state.simulator_state = hidden_state - # Note: this takes in Observation, GoalDescription, whose types are - # Any + state.simulator_state = {} + state.simulator_state["state"] = hidden_state + # A DefaultEnvironmentTask is a dummy environment task. Our render + # function does not use the task argument, so this is ok. + state.simulator_state["images"] = self.render_state( + state, DefaultEnvironmentTask) + # Recall that a EnvironmentTask consists of an Observation and a + # GoalDescription, both of whose types are Any. tasks.append(EnvironmentTask(state, goal)) return tasks @@ -235,13 +247,15 @@ def _AdjacentNotFacing_holds(self, state: State, def _IsCooked_holds(self, state: State, objects: Sequence[Object]) -> bool: patty, = objects - return state.simulator_state[patty][ # type: ignore[index] - "is_cooked"] > 0.5 + assert state.simulator_state is not None + assert "state" in state.simulator_state + return state.simulator_state["state"][patty]["is_cooked"] > 0.5 def _IsSliced_holds(self, state: State, objects: Sequence[Object]) -> bool: tomato, = objects - return state.simulator_state[tomato][ # type: ignore[index] - "is_sliced"] > 0.5 + assert state.simulator_state is not None + assert "state" in state.simulator_state + return state.simulator_state["state"][tomato]["is_sliced"] > 0.5 def _HandEmpty_holds(self, state: State, objects: Sequence[Object]) -> bool: @@ -250,10 +264,10 @@ def _HandEmpty_holds(self, state: State, def _Holding_holds(self, state: State, objects: Sequence[Object]) -> bool: robot, item = objects - return not self._HandEmpty_holds( - state, - [robot]) and state.simulator_state[item][ # type: ignore[index] - "is_held"] > 0.5 + assert state.simulator_state is not None + assert "state" in state.simulator_state + return not self._HandEmpty_holds(state,[robot]) and \ + state.simulator_state["state"][item]["is_held"] > 0.5 def _On_holds(self, state: State, objects: Sequence[Object]) -> bool: a, b = objects @@ -321,6 +335,7 @@ def predicates(self) -> Set[Predicate]: @property def goal_predicates(self) -> Set[Predicate]: return {self._On, self._IsCooked, self._IsSliced} + # return {self._On, self._GoalHack} @property def action_space(self) -> Box: @@ -378,8 +393,12 @@ def simulate(self, state: State, action: Action) -> State: # We assume only one of , , , # is not "null" in each action. # If each one was null, the action would be <0, 0, -1, 0, 0>. + assert state.simulator_state is not None + assert "state" in state.simulator_state assert self.action_space.contains(action.arr) next_state = state.copy() + assert next_state.simulator_state is not None + assert "state" in next_state.simulator_state dcol, drow, dir_from_turning, interact, pickplace = action.arr rx, ry = self.get_position(self._robot, state) @@ -403,11 +422,12 @@ def simulate(self, state: State, action: Action) -> State: ] for obj in other_objects: if obj in items: - if state.simulator_state[obj][ # type: ignore[index] - "is_held"] > 0.5: + if state.simulator_state["state"][obj]["is_held"] > 0.5: continue ox, oy = self.get_position(obj, state) if abs(new_rx - ox) < 1e-3 and abs(new_ry - oy) < 1e-3: + next_state.simulator_state["images"] = self.render_state( + next_state, DefaultEnvironmentTask) return next_state # No collision detected, so we can move the robot. @@ -416,8 +436,7 @@ def simulate(self, state: State, action: Action) -> State: # If an object was held, move it with the robot. for item in items: - if state.simulator_state[item][ # type: ignore[index] - "is_held"] > 0.5: + if state.simulator_state["state"][item]["is_held"] > 0.5: next_state.set(item, "col", new_rx) next_state.set(item, "row", new_ry) @@ -427,11 +446,11 @@ def simulate(self, state: State, action: Action) -> State: [self._robot, item]) and interact > 0.5: if item.is_instance(self._patty_type) and self._On_holds( state, [item, self._grill]): - next_state.simulator_state[item][ # type: ignore[index] + next_state.simulator_state["state"][item][ "is_cooked"] = 1.0 elif item.is_instance(self._tomato_type) and self._On_holds( state, [item, self._cutting_board]): - next_state.simulator_state[item][ # type: ignore[index] + next_state.simulator_state["state"][item][ "is_sliced"] = 1.0 # Handle picking. @@ -443,8 +462,7 @@ def simulate(self, state: State, action: Action) -> State: if len(facing_items) > 0: # We'll pick up the item that is "on top". on_top = max(facing_items, key=lambda x: x[1])[0] - next_state.simulator_state[on_top][ # type: ignore[index] - "is_held"] = 1.0 + next_state.simulator_state["state"][on_top]["is_held"] = 1.0 next_state.set(on_top, "col", rx) next_state.set(on_top, "row", ry) next_state.set(on_top, "z", 0) @@ -454,15 +472,13 @@ def simulate(self, state: State, action: Action) -> State: if pickplace > 0.5 and not self._HandEmpty_holds(state, [self._robot]): held_item = [ item for item in items - if state.simulator_state[item] # type: ignore[index] - ["is_held"] > 0.5 + if state.simulator_state["state"][item]["is_held"] > 0.5 ][0] px, py = self.get_cell_in_direction( rx, ry, self.enum_to_dir[state.get(self._robot, "dir")]) if 0 <= py <= self.num_rows and 0 <= px <= self.num_cols: next_state.set(self._robot, "fingers", 0.0) - next_state.simulator_state[held_item][ # type: ignore[index] - "is_held"] = 0.0 + next_state.simulator_state["state"][held_item]["is_held"] = 0.0 next_state.set(held_item, "col", px) next_state.set(held_item, "row", py) # If any other objects are at this location, then this must go @@ -478,6 +494,11 @@ def simulate(self, state: State, action: Action) -> State: new_z = 0 next_state.set(held_item, "z", new_z) + # Update the image + assert next_state.simulator_state is not None + next_state.simulator_state["images"] = self.render_state( + next_state, DefaultEnvironmentTask) + return next_state def render_state_plt( @@ -538,19 +559,20 @@ def render_state_plt( held_img_size = (0.3, 0.3) offset = held_img_size[1] * (1 / 3) items = [obj for obj in state if obj.is_instance(self._item_type)] + assert state.simulator_state is not None + assert "state" in state.simulator_state for item in items: img = type_to_img[item.type] - if "is_cooked" in state.simulator_state[ # type: ignore[index] + if "is_cooked" in state.simulator_state["state"][ item] and self._IsCooked_holds(state, [item]): img = mpimg.imread( utils.get_env_asset_path("imgs/cooked_patty.png")) - elif "is_sliced" in state.simulator_state[ # type: ignore[index] + elif "is_sliced" in state.simulator_state["state"][ item] and self._IsSliced_holds(state, [item]): img = mpimg.imread( utils.get_env_asset_path("imgs/sliced_tomato.png")) zorder = state.get(item, "z") - is_held = state.simulator_state[item][ # type: ignore[index] - "is_held"] > 0.5 + is_held = state.simulator_state["state"][item]["is_held"] > 0.5 x, y = self.get_position(item, state) # If the item is held, make it smaller so that it does obstruct the # robot. @@ -593,6 +615,59 @@ def render_state_plt( plt.tight_layout() return fig + def render_state(self, + state: State, + task: EnvironmentTask, + action: Optional[Action] = None, + caption: Optional[str] = None) -> Video: + fig = self.render_state_plt(state, task, action, caption) + # Create an in-memory binary stream. + buf = io.BytesIO() + fig.savefig(buf, format='png') + # Rewind the stream to the beginning so that it can be read from. + buf.seek(0) + img = Image.open(buf) + plt.close(fig) + + # Convert the image to RGB mode to save as JPEG, because + # JPEG format does not support images with an alpha channel + # (transparency). + img_rgb = img.convert("RGB") + jpeg_buf = io.BytesIO() + img_rgb.save(jpeg_buf, format="JPEG") + jpeg_buf.seek(0) + jpeg_img = Image.open(jpeg_buf) + + # If we return jpeg_img, we get this error: + # `ValueError: I/O operation on closed file`, so we copy it before + # closing the buffers. + ret_img = copy.deepcopy(jpeg_img) + ret_arr = np.array(ret_img) + + buf.close() + jpeg_buf.close() + return [ret_arr] + + def _copy_observation(self, obs: Observation) -> Observation: + return copy.deepcopy(obs) + + def get_observation(self) -> Observation: + return self._copy_observation(self._current_observation) + + def reset(self, train_or_test: str, task_idx: int) -> Observation: + # Rather than have the observation be the state + the image, we just + # package the image inside the state's simulator_state. + self._current_task = self.get_task(train_or_test, task_idx) + self._current_observation = self._current_task.init_obs + return self._copy_observation(self._current_observation) + + def step(self, action: Action) -> Observation: + # Rather than have the observation be the state + the image, we just + # package the image inside the state's simulator_state. + self._current_observation = self.simulate(self._current_observation, + action) + return self._copy_observation(self._current_observation) + def get_event_to_action_fn( self) -> Callable[[State, matplotlib.backend_bases.Event], Action]: diff --git a/predicators/envs/kitchen.py b/predicators/envs/kitchen.py index d4d9be3471..e9806a040e 100644 --- a/predicators/envs/kitchen.py +++ b/predicators/envs/kitchen.py @@ -343,6 +343,7 @@ def state_info_to_state(cls, state_info: Dict[str, Any]) -> State: "angle": angle } state = utils.create_state_from_dict(state_dict) + state.simulator_state = {} return state def goal_reached(self) -> bool: diff --git a/predicators/perception/kitchen_perceiver.py b/predicators/perception/kitchen_perceiver.py index 6637ceaf1f..577e4bd98d 100644 --- a/predicators/perception/kitchen_perceiver.py +++ b/predicators/perception/kitchen_perceiver.py @@ -52,7 +52,8 @@ def step(self, observation: Observation) -> State: def _observation_to_state(self, obs: Observation) -> State: state = KitchenEnv.state_info_to_state(obs["state_info"]) - state.simulator_state = obs["obs_images"] + assert state.simulator_state is not None + state.simulator_state["images"] = obs["obs_images"] return state def render_mental_images(self, observation: Observation, diff --git a/predicators/pretrained_model_interface.py b/predicators/pretrained_model_interface.py index 2fb5079835..ee4ac685d2 100644 --- a/predicators/pretrained_model_interface.py +++ b/predicators/pretrained_model_interface.py @@ -85,11 +85,13 @@ def sample_completions(self, img_hash_list: List[str] = [] for img in imgs: img_hash_list.append(str(imagehash.phash(img))) - # NOTE: it's very possible that this string gets too long and this - # causes significant problems for us. We can fix this when it - # comes up by hashing this string to a shorter string, using e.g. + # NOTE: it's possible that this string (the concatenated hashes of + # each image) is very long. This would make the final cache + # foldername long. In many operating systems, the maximum folder + # name length is 255 characters. To shorten this foldername more, we + # can hash this string into a shorter string. For example, look at # https://stackoverflow.com/questions/57263436/hash-like-string-shortener-with-decoder # pylint:disable=line-too-long - imgs_id = "".join(img_hash_list) + imgs_id = hash("".join(img_hash_list)) cache_foldername += f"{imgs_id}" cache_folderpath = os.path.join(CFG.pretrained_model_prompt_cache_dir, cache_foldername) diff --git a/predicators/utils.py b/predicators/utils.py index 04f53b18f5..b34ead4ac6 100644 --- a/predicators/utils.py +++ b/predicators/utils.py @@ -2246,8 +2246,8 @@ def query_vlm_for_atom_vals( # This only works if state.simulator_state is some list of images that the # vlm can be called on. assert state.simulator_state is not None - assert isinstance(state.simulator_state, List) - imgs = state.simulator_state + assert isinstance(state.simulator_state["images"], List) + imgs = state.simulator_state["images"] vlm_atoms = sorted(vlm_atoms) atom_queries_str = "\n* " atom_queries_str += "\n* ".join(atom.get_vlm_query_str() diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py index 520ddfd274..021785c277 100644 --- a/tests/datasets/test_datasets.py +++ b/tests/datasets/test_datasets.py @@ -733,7 +733,8 @@ def test_create_ground_atom_data_from_generated_demos(): dataset = create_dataset(env, train_tasks, options, predicates) assert len(dataset.trajectories) == 1 for state in dataset.trajectories[0].states: - state.simulator_state = [np.zeros((32, 32), dtype=np.uint8)] + state.simulator_state = {} + state.simulator_state["images"] = [np.zeros((32, 32), dtype=np.uint8)] vlm = _DummyVLM() vlm_dataset = create_ground_atom_data_from_generated_demos( dataset, env, predicates, train_tasks, vlm) diff --git a/tests/envs/test_burger.py b/tests/envs/test_burger.py index fc2693c864..4c45ffd35a 100644 --- a/tests/envs/test_burger.py +++ b/tests/envs/test_burger.py @@ -72,7 +72,7 @@ def test_burger(): grill = [obj for obj in task.init if obj.name == "grill"][0] patty = [obj for obj in task.init if obj.name == "patty"][0] - robot = [obj for obj in task.init if obj.name == "robby"][0] + robot = [obj for obj in task.init if obj.name == "robot"][0] tomato = [obj for obj in task.init if obj.name == "tomato"][0] cutting_board = [obj for obj in task.init if obj.name == "cutting_board"][0] diff --git a/tests/test_utils.py b/tests/test_utils.py index 127ae9748f..1c511a346a 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1125,7 +1125,9 @@ def _classifier2(state, objects): vlm_pred = VLMPredicate("IsFishy", [], lambda s, o: NotImplementedError, lambda o: "is_fishy") vlm_state = state.copy() - vlm_state.simulator_state = [np.zeros((30, 30, 3), dtype=np.uint8)] + vlm_state.simulator_state = { + "images": [np.zeros((30, 30, 3), dtype=np.uint8)] + } vlm_atoms_set = utils.abstract(vlm_state, [vlm_pred], _DummyVLM()) assert len(vlm_atoms_set) == 1 assert "IsFishy" in str(vlm_atoms_set) From 9b4490cb918895ab68f6a6fe149456b289947fcf Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Mon, 3 Jun 2024 10:22:28 -0400 Subject: [PATCH 08/38] Add scene labeling option that provides the VLM its label history (#1661) * Initial commit. * Add new labeling prompt. * Get more tests to pass. * Run autoformatter. * Get rid of newline. --- .../datasets/generate_atom_trajs_with_vlm.py | 50 +++++++++++-------- .../img_option_diffs_label_history.txt | 3 ++ tests/datasets/test_datasets.py | 3 +- 3 files changed, 35 insertions(+), 21 deletions(-) create mode 100644 predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt diff --git a/predicators/datasets/generate_atom_trajs_with_vlm.py b/predicators/datasets/generate_atom_trajs_with_vlm.py index e1aee674aa..31311478d6 100644 --- a/predicators/datasets/generate_atom_trajs_with_vlm.py +++ b/predicators/datasets/generate_atom_trajs_with_vlm.py @@ -7,7 +7,7 @@ import re from functools import partial from pathlib import Path -from typing import Dict, List, Optional, Sequence, Set, Tuple +from typing import Dict, Iterator, List, Optional, Sequence, Set, Tuple import dill as pkl import numpy as np @@ -77,8 +77,9 @@ def _generate_prompt_for_atom_proposals( def _generate_prompt_for_scene_labelling( - traj: ImageOptionTrajectory, - atoms_list: List[str]) -> List[Tuple[str, List[PIL.Image.Image]]]: + traj: ImageOptionTrajectory, atoms_list: List[str], + label_history: List[str] +) -> Iterator[Tuple[str, List[PIL.Image.Image]]]: """Prompt for generating labels for an entire trajectory. Similar to the above prompting method, this outputs a list of prompts to label the state at each timestep of traj with atom values). @@ -86,7 +87,6 @@ def _generate_prompt_for_scene_labelling( Note that all our prompts are saved as separate txt files under the 'vlm_input_data_prompts/atom_labelling' folder. """ - ret_list = [] filepath_prefix = utils.get_path_to_predicators_root() + \ "/predicators/datasets/vlm_input_data_prompts/atom_labelling/" try: @@ -98,7 +98,13 @@ def _generate_prompt_for_scene_labelling( except FileNotFoundError: raise ValueError("Unknown VLM prompting option " + f"{CFG.grammar_search_vlm_atom_label_prompt_type}") - if CFG.grammar_search_vlm_atom_label_prompt_type == "img_option_diffs": + # The prompt ends with a section for 'Predicates', so list these. + for atom_str in atoms_list: + prompt += f"\n{atom_str}" + + if CFG.grammar_search_vlm_atom_label_prompt_type in [ + "img_option_diffs", "img_option_diffs_label_history" + ]: # In this case, we need to load the 'per_scene_naive' prompt as well # for the first timestep. with open(filepath_prefix + "per_scene_naive.txt", @@ -107,32 +113,36 @@ def _generate_prompt_for_scene_labelling( init_prompt = f.read() for atom_str in atoms_list: init_prompt += f"\n{atom_str}" - ret_list.append((init_prompt, traj.imgs[0])) + if len(label_history) == 0: + yield (init_prompt, traj.imgs[0]) + # Now, we use actual difference-based prompting for the second timestep # and beyond. - # The prompt ends with a section for 'Predicates', so list these. - for atom_str in atoms_list: - prompt += f"\n{atom_str}" for i in range(1, len(traj.imgs)): curr_prompt = prompt[:] - # NOTE: we rip out just one img from each of the state - # images. This is fine/works for the case where we only - # have one camera view, but probably will need to be - # amended in the future! + # Here, we want to give the VLM the images corresponding to the + # states at timestep t-1 and timestep t. Note that in the general + # case, there can be multiple images associated with each state, but + # for now we assume that there is just a single image. So, here we + # give the VLM the image from the state at t-1, and the image from + # the state at t. curr_prompt_imgs = [ imgs_timestep[0] for imgs_timestep in traj.imgs[i - 1:i + 1] ] curr_prompt += "\n\nSkill executed between states: " curr_prompt += traj.actions[i - 1].name + str( traj.actions[i - 1].objects) - ret_list.append((curr_prompt, curr_prompt_imgs)) + + if CFG.grammar_search_vlm_atom_label_prompt_type == \ + "img_option_diffs_label_history": + curr_prompt += "\n\nPredicate values in the first scene, " \ + "before the skill was executed: \n" + curr_prompt += label_history[-1] + yield (curr_prompt, curr_prompt_imgs) else: - for atom_str in atoms_list: - prompt += f"\n{atom_str}" for curr_imgs in traj.imgs: # NOTE: same problem with ripping out images as in the above note. - ret_list.append((prompt, [curr_imgs[0]])) - return ret_list + yield (prompt, [curr_imgs[0]]) def _sample_vlm_atom_proposals_from_trajectories( @@ -171,9 +181,9 @@ def _label_trajectories_with_vlm_atom_values( curr_scenes_labelled = 0 output_labelled_atoms_txt_list = [] for traj in trajectories: + curr_traj_txt_outputs: List[str] = [] prompts_for_traj = _generate_prompt_for_scene_labelling( - traj, atoms_list) - curr_traj_txt_outputs = [] + traj, atoms_list, label_history=curr_traj_txt_outputs) for text_prompt, img_prompt in prompts_for_traj: # Sample VLM outputs with temperature 0 in an attempt to be # accurate. diff --git a/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt new file mode 100644 index 0000000000..98a17aa5d3 --- /dev/null +++ b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt @@ -0,0 +1,3 @@ +You are a vision system for a robot. You are provided with two images corresponding to the states before and after a particular skill is executed. You are given a list of predicates below, and you are given the values of these predicates in the first scene, before the skill is executed. Your job is to output the values of the following predicates in only the final scene. Pay careful attention to the changes between the two images to figure out which predicates change and which predicates do not change. For the predicates that change, list these separately at the end of your response. Note that in some scenes, there might be no changes. Output each predicate value as a bulleted list with each predicate and value on a different line. For each output value, provide an explanation as to why you labelled this predicate as having this particular value. Use the format: : . . + +Predicates: \ No newline at end of file diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py index 021785c277..371d2e94e7 100644 --- a/tests/datasets/test_datasets.py +++ b/tests/datasets/test_datasets.py @@ -593,7 +593,8 @@ def test_loading_saved_vlm_img_demos_folder_non_dummy_goal(): ("naive_whole_traj", "per_scene_cot"), ("not_a_real_prompt_type", "per_scene_cot"), ("naive_whole_traj", "not_a_real_prompt_type"), - ("options_labels_whole_traj", "img_option_diffs")]) + ("options_labels_whole_traj", "img_option_diffs"), + ("options_labels_whole_traj", "img_option_diffs_label_history")]) def test_loading_saved_vlm_img_demos_folder_dummy_goal( atom_proposal_prompt_type, atom_labelling_prompt_type): """Test loading a dataset from img demo files.""" From 10b442c2cb43e4e2874c3ef0577016ab278513e2 Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Mon, 3 Jun 2024 15:26:06 -0400 Subject: [PATCH 09/38] Ask vlm to describe changes in the scene before labeling predicates (#1662) * Initial commit. * Keep only the prompt change, and not the before/after text on the before/after images. * Delete unused code. * Remove note that the images are labeled with before/after, since we're not doing that annotation of the images in this PR. --- predicators/datasets/generate_atom_trajs_with_vlm.py | 3 ++- .../atom_labelling/img_option_diffs_label_history.txt | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/predicators/datasets/generate_atom_trajs_with_vlm.py b/predicators/datasets/generate_atom_trajs_with_vlm.py index 31311478d6..372c491c99 100644 --- a/predicators/datasets/generate_atom_trajs_with_vlm.py +++ b/predicators/datasets/generate_atom_trajs_with_vlm.py @@ -130,8 +130,9 @@ def _generate_prompt_for_scene_labelling( imgs_timestep[0] for imgs_timestep in traj.imgs[i - 1:i + 1] ] curr_prompt += "\n\nSkill executed between states: " - curr_prompt += traj.actions[i - 1].name + str( + skill_name = traj.actions[i - 1].name + str( traj.actions[i - 1].objects) + curr_prompt += skill_name if CFG.grammar_search_vlm_atom_label_prompt_type == \ "img_option_diffs_label_history": diff --git a/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt index 98a17aa5d3..aaef54b9b7 100644 --- a/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt +++ b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt @@ -1,3 +1,3 @@ -You are a vision system for a robot. You are provided with two images corresponding to the states before and after a particular skill is executed. You are given a list of predicates below, and you are given the values of these predicates in the first scene, before the skill is executed. Your job is to output the values of the following predicates in only the final scene. Pay careful attention to the changes between the two images to figure out which predicates change and which predicates do not change. For the predicates that change, list these separately at the end of your response. Note that in some scenes, there might be no changes. Output each predicate value as a bulleted list with each predicate and value on a different line. For each output value, provide an explanation as to why you labelled this predicate as having this particular value. Use the format: : . . +You are a vision system for a robot. You are provided with two images corresponding to the states before and after a particular skill is executed. You are given a list of predicates below, and you are given the values of these predicates in the image before the skill is executed. Your job is to output the values of the following predicates in the image after the skill is executed. Pay careful attention to the changes between the two images to figure out which predicates change and which predicates do not change. For the predicates that change, list these separately at the end of your response. Note that in some scenes, there might be no changes. First, output a description of what changes happen between the before and after images. Next, output each predicate value in the after image as a bulleted list with each predicate and value on a different line. For each predicate value, provide an explanation as to why you labelled this predicate as having this particular value. Use the format: : . . Predicates: \ No newline at end of file From 2e0a2286de8ec484dc275e0119f4047f316c7296 Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Wed, 5 Jun 2024 11:54:51 -0400 Subject: [PATCH 10/38] Allow different task goals for demonstrator vs agent (#1663) * Initial commit. * Get test time different goal for agent vs demonstrator to work. * Get more checks to pass. * Get more tests to pass. * Remove unnecessary changes. * Add tests for more coverage. * Run autoformatter. * Fix test. --- predicators/envs/burger.py | 31 +++++++++++++++++-------- predicators/main.py | 16 ++++++++++--- predicators/settings.py | 5 ++++ predicators/structs.py | 47 +++++++++++++++++++++++++++++++++++++- predicators/utils.py | 24 +++++++++---------- tests/test_structs.py | 33 ++++++++++++++++++++++++-- tests/test_utils.py | 12 ++++++++-- 7 files changed, 138 insertions(+), 30 deletions(-) diff --git a/predicators/envs/burger.py b/predicators/envs/burger.py index 8c3581e977..37dc163062 100644 --- a/predicators/envs/burger.py +++ b/predicators/envs/burger.py @@ -176,14 +176,24 @@ def _get_tasks(self, num: int, hidden_state[bottom_bun] = {"is_held": 0.0} goal = { + # GroundAtom(self._On, [patty, bottom_bun]), + # GroundAtom(self._On, [cheese, patty]), + # GroundAtom(self._On, [tomato, cheese]), + # GroundAtom(self._On, [top_bun, tomato]), + # GroundAtom(self._IsCooked, [patty]), + # GroundAtom(self._IsSliced, [tomato]), + # GroundAtom(self._GoalHack, [bottom_bun, patty, cheese, tomato, + # top_bun]) GroundAtom(self._On, [patty, bottom_bun]), GroundAtom(self._On, [cheese, patty]), - GroundAtom(self._On, [tomato, cheese]), - GroundAtom(self._On, [top_bun, tomato]), GroundAtom(self._IsCooked, [patty]), - GroundAtom(self._IsSliced, [tomato]), - # GroundAtom(self._GoalHack, [bottom_bun, patty, cheese, tomato, - # top_bun]) + } + + alt_goal = { + GroundAtom(self._On, [patty, bottom_bun]), + GroundAtom(self._On, [cheese, patty]), + GroundAtom(self._GoalHack, + [bottom_bun, patty, cheese, tomato, top_bun]) } for _ in range(num): @@ -196,7 +206,7 @@ def _get_tasks(self, num: int, state, DefaultEnvironmentTask) # Recall that a EnvironmentTask consists of an Observation and a # GoalDescription, both of whose types are Any. - tasks.append(EnvironmentTask(state, goal)) + tasks.append(EnvironmentTask(state, goal, alt_goal_desc=alt_goal)) return tasks @@ -297,14 +307,15 @@ def _Clear_holds(self, state: State, objects: Sequence[Object]) -> bool: return True def _GoalHack_holds(self, state: State, objects: Sequence[Object]) -> bool: - bottom, patty, cheese, tomato, top = objects + # bottom, patty, cheese, tomato, top = objects + bottom, patty, cheese, _, _ = objects atoms = [ self._On_holds(state, [patty, bottom]), self._On_holds(state, [cheese, patty]), - self._On_holds(state, [tomato, cheese]), - self._On_holds(state, [top, tomato]), + # self._On_holds(state, [tomato, cheese]), + # self._On_holds(state, [top, tomato]), self._IsCooked_holds(state, [patty]), - self._IsSliced_holds(state, [tomato]) + # self._IsSliced_holds(state, [tomato]) ] return all(atoms) diff --git a/predicators/main.py b/predicators/main.py index ce1a474181..7b3cd4657c 100644 --- a/predicators/main.py +++ b/predicators/main.py @@ -112,6 +112,14 @@ def main() -> None: stripped_train_tasks = [ utils.strip_task(task, preds) for task in train_tasks ] + # If the goals of the tasks that the approaches solve need to be described + # using predicates that differ from those in the goals of the tasks that the + # demonstrator solves, then replace those predicates accordingly. This is + # used in VLM predicate invention where we want to invent certain goal + # predicates that the demonstrator needed to solve the task. + approach_train_tasks = [ + task.replace_goal_with_alt_goal() for task in stripped_train_tasks + ] if CFG.option_learner == "no_learning": # If we are not doing option learning, pass in all the environment's # oracle options. @@ -124,7 +132,7 @@ def main() -> None: if CFG.approach_wrapper: approach_name = f"{CFG.approach_wrapper}[{approach_name}]" approach = create_approach(approach_name, preds, options, env.types, - env.action_space, stripped_train_tasks) + env.action_space, approach_train_tasks) if approach.is_learning_based: # Create the offline dataset. Note that this needs to be done using # the non-stripped train tasks because dataset generation may need @@ -136,7 +144,7 @@ def main() -> None: execution_monitor = create_execution_monitor(CFG.execution_monitor) cogman = CogMan(approach, perceiver, execution_monitor) # Run the full pipeline. - _run_pipeline(env, cogman, stripped_train_tasks, offline_dataset) + _run_pipeline(env, cogman, approach_train_tasks, offline_dataset) script_time = time.perf_counter() - script_start logging.info(f"\n\nMain script terminated in {script_time:.5f} seconds") @@ -309,7 +317,9 @@ def _generate_interaction_results( def _run_testing(env: BaseEnv, cogman: CogMan) -> Metrics: - test_tasks = env.get_test_tasks() + test_tasks = [ + task.replace_goal_with_alt_goal() for task in env.get_test_tasks() + ] num_found_policy = 0 num_solved = 0 cogman.reset_metrics() diff --git a/predicators/settings.py b/predicators/settings.py index 9b5ad95d4f..2091cddddb 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -46,6 +46,11 @@ class GlobalSettings: # your call to utils.reset_config(). render_state_dpi = 150 approach_wrapper = None + # Normally, excluding goal predicates does not make sense, because then + # there is no goal for the agent to plan towards. This is intended to be + # used by VLM predicate invention, where we want to invent goal predicates + # and different task goals are provided to the agent and the demonstrator. + allow_exclude_goal_predicates = False # cover_multistep_options env parameters cover_multistep_action_limits = [-np.inf, np.inf] diff --git a/predicators/structs.py b/predicators/structs.py index 889905793d..b77f2ce353 100644 --- a/predicators/structs.py +++ b/predicators/structs.py @@ -430,6 +430,12 @@ class Task: """Struct defining a task, which is an initial state and goal.""" init: State goal: Set[GroundAtom] + # Sometimes we want the task presented to the agent to have goals described + # in terms of predicates that are different than those describing the goal + # of the task presented to the demonstrator. In these cases, we will store + # an "alternative goal" in this field and replace the goal with the + # alternative goal before giving the task to the agent. + alt_goal: Optional[Set[GroundAtom]] = field(default_factory=set) def __post_init__(self) -> None: # Verify types. @@ -440,6 +446,16 @@ def goal_holds(self, state: State) -> bool: """Return whether the goal of this task holds in the given state.""" return all(goal_atom.holds(state) for goal_atom in self.goal) + def replace_goal_with_alt_goal(self) -> Task: + """Return a Task with the goal replaced with the alternative goal if it + exists.""" + # We may not want the agent to access the goal predicates given to the + # demonstrator. To prevent leakage of this information, we discard the + # original goal. + if self.alt_goal: + return Task(self.init, goal=self.alt_goal) + return self + DefaultTask = Task(DefaultState, set()) @@ -456,11 +472,28 @@ class EnvironmentTask: """ init_obs: Observation goal_description: GoalDescription + # See Task._alt_goal for the reason for this field. + alt_goal_desc: Optional[GoalDescription] = field(default=None) @cached_property def task(self) -> Task: """Convenience method for environment tasks that are fully observed.""" - return Task(self.init, self.goal) + # If the environment task's goal is replaced with the alternative goal + # before turning the environment task into a task, or no alternative + # goal exists, then there's nothing particular to set the task's + # alt_goal field to. + if self.alt_goal_desc is None: + return Task(self.init, self.goal) + # If we turn the environment task into a task before replacing the goal + # with the alternative goal, we have to set the task's alt_goal field + # accordingly to leave open the possibility of doing that replacement + # later. + # Assumption: we currently assume the alternative goal description is + # always a set of ground atoms. + assert isinstance(self.alt_goal_desc, set) + for atom in self.alt_goal_desc: + assert isinstance(atom, GroundAtom) + return Task(self.init, self.goal, alt_goal=self.alt_goal_desc) @cached_property def init(self) -> State: @@ -476,6 +509,18 @@ def goal(self) -> Set[GroundAtom]: next(iter(self.goal_description)), GroundAtom) return self.goal_description + def replace_goal_with_alt_goal(self) -> EnvironmentTask: + """Return an EnvironmentTask with the goal description replaced with + the alternative goal description if it exists. + + See Task.replace_goal_with_alt_goal for the reason for this + function. + """ + if self.alt_goal_desc is not None: + return EnvironmentTask(self.init_obs, + goal_description=self.alt_goal_desc) + return self + DefaultEnvironmentTask = EnvironmentTask(DefaultState, set()) diff --git a/predicators/utils.py b/predicators/utils.py index b34ead4ac6..bcbd7d91e6 100644 --- a/predicators/utils.py +++ b/predicators/utils.py @@ -2199,19 +2199,17 @@ def _stripped_classifier(state: State, objects: Sequence[Object]) -> bool: def strip_task(task: Task, included_predicates: Set[Predicate]) -> Task: - """Create a new task where any excluded predicates have their classifiers - removed.""" + """Create a new task where any excluded goal predicates have their + classifiers removed.""" stripped_goal: Set[GroundAtom] = set() for atom in task.goal: - # The atom's goal is known. if atom.predicate in included_predicates: stripped_goal.add(atom) continue - # The atom's goal is unknown. stripped_pred = strip_predicate(atom.predicate) stripped_atom = GroundAtom(stripped_pred, atom.objects) stripped_goal.add(stripped_atom) - return Task(task.init, stripped_goal) + return Task(task.init, stripped_goal, alt_goal=task.alt_goal) def create_vlm_predicate( @@ -3500,7 +3498,11 @@ def parse_config_excluded_predicates( for pred in env.predicates if pred.name not in excluded_names } if CFG.offline_data_method != "demo+ground_atoms": - assert env.goal_predicates.issubset(included), \ + if CFG.allow_exclude_goal_predicates: + if not env.goal_predicates.issubset(included): + logging.info("Note: excluding goal predicates!") + else: + assert env.goal_predicates.issubset(included), \ "Can't exclude a goal predicate!" else: excluded_names = set() @@ -3825,12 +3827,10 @@ def add_text_to_draw_img( ) # Slightly larger than text background_size = (text_width + 10, text_height + 10) # Draw the background rectangle - draw.rectangle([ - background_position, - (background_position[0] + background_size[0], - background_position[1] + background_size[1]) - ], - fill="black") + draw.rectangle( + (background_position, (background_position[0] + background_size[0], + background_position[1] + background_size[1])), + fill="black") # Add the text to the image draw.text(position, text, fill="red", font=font) return draw diff --git a/tests/test_structs.py b/tests/test_structs.py index 37c977243d..e1181d000b 100644 --- a/tests/test_structs.py +++ b/tests/test_structs.py @@ -6,8 +6,8 @@ from predicators import utils from predicators.structs import NSRT, PNAD, Action, DefaultState, \ - DemonstrationQuery, DummyOption, GroundAtom, GroundMacro, \ - InteractionRequest, InteractionResult, LDLRule, LiftedAtom, \ + DemonstrationQuery, DummyOption, EnvironmentTask, GroundAtom, \ + GroundMacro, InteractionRequest, InteractionResult, LDLRule, LiftedAtom, \ LiftedDecisionList, LowLevelTrajectory, Macro, Object, \ ParameterizedOption, Predicate, Query, Segment, State, STRIPSOperator, \ Task, Type, Variable, _Atom, _GroundNSRT, _GroundSTRIPSOperator, _Option @@ -300,6 +300,35 @@ def test_task(state): assert task2.init.allclose(state) assert task2.goal == goal2 assert not task2.goal_holds(task.init) + # Test replacing goal with alternative goal. + pred3 = Predicate("AlternativeOn", [cup_type, plate_type], + lambda s, o: True) + goal3 = {pred3([cup, plate])} + task3 = Task(state, goal=goal, alt_goal=goal3) + alt_task = task3.replace_goal_with_alt_goal() + assert alt_task.goal == goal3 + + +def test_environment_task(state): + """Tests for EnvironmentTask class.""" + # Test replacing goal with alternative goal. + cup_type = Type("cup_type", ["feat1"]) + cup = cup_type("cup") + plate_type = Type("plate_type", ["feat1"]) + plate = plate_type("plate") + pred = Predicate("On", [cup_type, plate_type], lambda s, o: True) + pred2 = Predicate("AlternativeOn", [cup_type, plate_type], + lambda s, o: True) + goal = {pred([cup, plate])} + alt_goal = {pred2([cup, plate])} + env_task = EnvironmentTask(state, + goal_description=goal, + alt_goal_desc=alt_goal) + task = env_task.task + assert task.alt_goal == alt_goal + alt_env_task = env_task.replace_goal_with_alt_goal() + assert alt_env_task.goal_description == alt_goal + assert alt_env_task.alt_goal_desc is None def test_option(state): diff --git a/tests/test_utils.py b/tests/test_utils.py index 1c511a346a..7fa10b9cb3 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1028,7 +1028,7 @@ def test_strip_task(): """Test for strip_task().""" env = CoverEnv() Covers, Holding = _get_predicates_by_names("cover", ["Covers", "Holding"]) - task = env.get_train_tasks()[0] + task = env.get_train_tasks()[0].task block0, _, _, target0, _ = list(task.init) # Goal is Covers(block0, target0) assert len(task.goal) == 1 @@ -3236,13 +3236,21 @@ def test_parse_config_excluded_predicates(): "HandEmpty", "Holding", "IsBlock", "IsTarget" ] assert sorted(p.name for p in excluded) == ["Covers"] - # Cannot exclude goal predicates otherwise.. + # Cannot exclude goal predicates. utils.reset_config({ "offline_data_method": "demo", "excluded_predicates": "Covers", }) with pytest.raises(AssertionError): utils.parse_config_excluded_predicates(env) + # Can exclude goal predicates if allowed per the settings. + utils.reset_config({ + "offline_data_method": "demo", + "excluded_predicates": "Covers", + "allow_exclude_goal_predicates": True + }) + included, excluded = utils.parse_config_excluded_predicates(env) + assert [p.name for p in excluded] == ["Covers"] def test_null_sampler(): From 161f250a486a86842a46d298143a055f1c414b62 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Thu, 6 Jun 2024 16:28:08 -0400 Subject: [PATCH 11/38] Kitchen init state randomization (#1664) * init changes to start hacking on things! * steady progress * todo: just get randomization to work along burner axis. * works! * remove unnec change * ready to go --- predicators/envs/kitchen.py | 17 ++++++++++++++--- .../ground_truth_models/kitchen/nsrts.py | 4 +--- predicators/settings.py | 1 + 3 files changed, 16 insertions(+), 6 deletions(-) diff --git a/predicators/envs/kitchen.py b/predicators/envs/kitchen.py index e9806a040e..bcf6547224 100644 --- a/predicators/envs/kitchen.py +++ b/predicators/envs/kitchen.py @@ -40,6 +40,10 @@ } _TRACKED_BODIES = ["Burner 1", "Burner 2", "Burner 3", "Burner 4"] +KETTLE_ON_BURNER1_POS = [0.169, 0.35, 1.626] +KETTLE_ON_BURNER2_POS = [-0.269, 0.35, 1.626] +KETTLE_ON_BURNER3_POS = [0.169, 0.65, 1.626] +KETTLE_ON_BURNER4_POS = [-0.269, 0.65, 1.626] class KitchenEnv(BaseEnv): @@ -86,9 +90,9 @@ class KitchenEnv(BaseEnv): at_pre_pushontop_x_atol = 1.0 # other tolerance for AtPrePushOnTop obj_name_to_pre_push_dpos = { - ("kettle", "on"): (-0.05, -0.3, -0.12), + ("kettle", "on"): (-0.05, -0.25, 0.00), ("kettle", "off"): (0.0, 0.0, 0.08), - ("knob4", "on"): (-0.1, -0.10, 0.05), + ("knob4", "on"): (-0.1, -0.12, 0.05), ("knob4", "off"): (0.05, -0.12, -0.05), ("light", "on"): (0.1, -0.05, -0.05), ("light", "off"): (-0.1, -0.05, -0.05), @@ -105,7 +109,7 @@ class KitchenEnv(BaseEnv): def __init__(self, use_gui: bool = True) -> None: super().__init__(use_gui) assert _MJKITCHEN_IMPORTED, "Failed to import kitchen gym env. \ -Install from https://github.com/SiddarGu/Gymnasium-Robotics.git. \ +Install from https://github.com/NishanthJKumar/Gymnasium-Robotics. \ BE SURE TO INSTALL FROM GITHUB SOURCE THOUGH; do not blindly install as the \ README of that repo suggests!" @@ -405,6 +409,13 @@ def _get_tasks(self, num: int, def _reset_initial_state_from_seed(self, seed: int) -> Observation: self._gym_env.reset(seed=seed) + if CFG.kitchen_randomize_init_state: + rng = np.random.default_rng(seed) + # For now, we only randomize the state such that the kettle + # is anywhere between burners 2 and 4. Later, we might add + # even more variation. + kettle_coords = (-0.269, rng.uniform(0.4, 0.55), 1.626) + self._gym_env.set_body_position("kettle", kettle_coords) return { "state_info": self.get_object_centric_state_info(), "obs_images": self.render() diff --git a/predicators/ground_truth_models/kitchen/nsrts.py b/predicators/ground_truth_models/kitchen/nsrts.py index 94b58dcdc8..66acef622a 100644 --- a/predicators/ground_truth_models/kitchen/nsrts.py +++ b/predicators/ground_truth_models/kitchen/nsrts.py @@ -232,7 +232,6 @@ def move_and_push_kettle_sampler(state: State, goal: Set[GroundAtom], preconditions = { LiftedAtom(NotOnTop, [kettle, surface_to]), LiftedAtom(BurnerAhead, [surface_to, surface_from]), - LiftedAtom(OnTop, [kettle, surface_from]), } add_effects = {LiftedAtom(OnTop, [kettle, surface_to])} delete_effects = {LiftedAtom(NotOnTop, [kettle, surface_to])} @@ -277,7 +276,6 @@ def move_and_push_kettle_sampler(state: State, goal: Set[GroundAtom], preconditions = { LiftedAtom(NotOnTop, [kettle, surface_to]), LiftedAtom(BurnerAhead, [surface_to, surface_from]), - LiftedAtom(OnTop, [kettle, surface_from]), LiftedAtom(TurnedOn, [knob]), LiftedAtom(KnobAndBurnerLinked, [knob, surface_to]) } @@ -287,7 +285,7 @@ def move_and_push_kettle_sampler(state: State, goal: Set[GroundAtom], } delete_effects = {LiftedAtom(NotOnTop, [kettle, surface_to])} ignore_effects = { - AtPreTurnOn, AtPrePushOnTop, AtPreTurnOff, AtPrePullKettle + AtPreTurnOn, AtPrePushOnTop, AtPreTurnOff, AtPrePullKettle, OnTop } option = PushKettleOntoBurner option_vars = [gripper, kettle, surface_to] diff --git a/predicators/settings.py b/predicators/settings.py index 2091cddddb..be81264d02 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -329,6 +329,7 @@ class GlobalSettings: kitchen_goals = "all" kitchen_render_set_of_marks = False kitchen_use_combo_move_nsrts = False + kitchen_randomize_init_state = False # sticky table env parameters sticky_table_num_tables = 5 From 3f9af164e9e9d674c02ac7ffbaef381fd7283eec Mon Sep 17 00:00:00 2001 From: Alicia Li <102700848+matrixbalto@users.noreply.github.com> Date: Tue, 11 Jun 2024 18:23:20 -0400 Subject: [PATCH 12/38] Created rl_bridge_approach and rendering for grid_row_door (#1665) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * adding rl_bridge_approach * type check * minor fixes * fix test * small fixes * small formatting change * more fixes * cleaned up rl_bridge * formatting * fixed comments --------- Co-authored-by: “matrixbalto” <“ilfangliu@gmail.com”> Co-authored-by: Nishanth Kumar --- .../approaches/bridge_policy_approach.py | 154 +++++++++++++++++- predicators/approaches/maple_q_approach.py | 23 ++- predicators/envs/grid_row.py | 77 ++++++++- predicators/ml_models.py | 27 ++- predicators/settings.py | 4 + .../approaches/test_bridge_policy_approach.py | 54 +++++- tests/approaches/test_maple_q_approach.py | 1 + tests/envs/test_grid_row.py | 20 ++- tests/test_ml_models.py | 6 +- 9 files changed, 338 insertions(+), 28 deletions(-) diff --git a/predicators/approaches/bridge_policy_approach.py b/predicators/approaches/bridge_policy_approach.py index 46810f9110..09f06e9ec9 100644 --- a/predicators/approaches/bridge_policy_approach.py +++ b/predicators/approaches/bridge_policy_approach.py @@ -48,6 +48,7 @@ from predicators import utils from predicators.approaches import ApproachFailure, ApproachTimeout +from predicators.approaches.maple_q_approach import MapleQApproach from predicators.approaches.oracle_approach import OracleApproach from predicators.bridge_policies import BridgePolicyDone, create_bridge_policy from predicators.nsrt_learning.segmentation import segment_trajectory @@ -55,8 +56,8 @@ from predicators.settings import CFG from predicators.structs import NSRT, Action, BridgeDataset, DefaultState, \ DemonstrationQuery, DemonstrationResponse, InteractionRequest, \ - InteractionResult, ParameterizedOption, Predicate, Query, State, Task, \ - Type, _Option + InteractionResult, LowLevelTrajectory, ParameterizedOption, Predicate, \ + Query, State, Task, Type, _GroundNSRT, _Option from predicators.utils import OptionExecutionFailure @@ -346,3 +347,152 @@ def learn_from_interaction_results( )) return self._bridge_policy.learn_from_demos(self._bridge_dataset) + + +class RLBridgePolicyApproach(BridgePolicyApproach): + """A simulator-free bilevel planning approach that uses a Deep RL (Maple Q) + bridge policy.""" + + def __init__(self, + initial_predicates: Set[Predicate], + initial_options: Set[ParameterizedOption], + types: Set[Type], + action_space: Box, + train_tasks: List[Task], + task_planning_heuristic: str = "default", + max_skeletons_optimized: int = -1) -> None: + super().__init__(initial_predicates, initial_options, types, + action_space, train_tasks, task_planning_heuristic, + max_skeletons_optimized) + self._maple_initialized = False + self._trajs: List[LowLevelTrajectory] = [] + self.mapleq=MapleQApproach(self._get_current_predicates(), \ + self._initial_options, self._types, \ + self._action_space, self._train_tasks) + + @classmethod + def get_name(cls) -> str: + return "rl_bridge_policy" + + @property + def is_learning_based(self) -> bool: + return True + + def _init_nsrts(self) -> None: + """Initializing nsrts for MAPLE Q.""" + nsrts = self._get_current_nsrts() + predicates = self._get_current_predicates() + all_ground_nsrts: Set[_GroundNSRT] = set() + if CFG.sesame_grounder == "naive": + for nsrt in nsrts: + all_objects = {o for t in self._train_tasks for o in t.init} + all_ground_nsrts.update( + utils.all_ground_nsrts(nsrt, all_objects)) + elif CFG.sesame_grounder == "fd_translator": # pragma: no cover + all_objects = set() + for t in self.mapleq._train_tasks: # pylint: disable=protected-access + curr_task_objects = set(t.init) + curr_task_types = {o.type for o in t.init} + curr_init_atoms = utils.abstract(t.init, predicates) + all_ground_nsrts.update( + utils.all_ground_nsrts_fd_translator( + nsrts, curr_task_objects, predicates, curr_task_types, + curr_init_atoms, t.goal)) + all_objects.update(curr_task_objects) + else: # pragma: no cover + raise ValueError( + f"Unrecognized sesame_grounder: {CFG.sesame_grounder}") + goals = [t.goal for t in self.mapleq._train_tasks] # pylint: disable=protected-access + self.mapleq._q_function.set_grounding( # pylint: disable=protected-access + all_objects, goals, all_ground_nsrts) + + def _solve(self, + task: Task, + timeout: int, + train_or_test: str = "test") -> Callable[[State], Action]: + if not self._maple_initialized: + self.mapleq = MapleQApproach(self._get_current_predicates(), + self._initial_options, self._types, + self._action_space, self._train_tasks) + self._maple_initialized = True + self._init_nsrts() + + # Start by planning. Note that we cannot start with the bridge policy + # because the bridge policy takes as input the last failed NSRT. + current_control = "planner" + option_policy = self._get_option_policy_by_planning(task, timeout) + current_policy = utils.option_policy_to_policy( + option_policy, + max_option_steps=CFG.max_num_steps_option_rollout, + raise_error_on_repeated_state=True, + ) + all_failed_options: List[_Option] = [] + + # Prevent infinite loops by detecting if the bridge policy is called + # twice with the same state. + last_bridge_policy_state = DefaultState + + def _policy(s: State) -> Action: + nonlocal current_control, current_policy, last_bridge_policy_state + + # Normal execution. Either keep executing the current option, or + # switch to the next option if it has terminated. + try: + action = current_policy(s) + return action + except OptionExecutionFailure as e: + failed_option = e.info["last_failed_option"] + if failed_option is not None: + all_failed_options.append(failed_option) + + # Switch control from planner to bridge. + assert current_control == "planner" + current_control = "bridge" + + current_policy = self.mapleq._solve( # pylint: disable=protected-access + task, timeout, train_or_test) + action = current_policy(s) + return action + + return _policy + + def _create_interaction_request(self, + train_task_idx: int) -> InteractionRequest: + task = self._train_tasks[train_task_idx] + policy = self._solve(task, timeout=CFG.timeout, train_or_test="train") + + reached_stuck_state = False + all_failed_options = None + + def _act_policy(s: State) -> Action: + nonlocal reached_stuck_state, all_failed_options + return policy(s) + + def _termination_fn(s: State) -> bool: + return task.goal_holds(s) + + # The request's acting policy is from mapleq + # The resulting trajectory is from maple q's sampling + request = InteractionRequest(train_task_idx, _act_policy, + lambda s: None, _termination_fn) + return request + + def learn_from_interaction_results( + self, results: Sequence[InteractionResult]) -> None: + # Turn state action pairs from results into trajectories + # If we haven't collected any new results on this cycle, skip learning + # for efficiency. + if not results: + return None + all_states = [] + all_actions = [] + + for result in results: + new_traj = LowLevelTrajectory(result.states, result.actions) + self._trajs.append(new_traj) + all_states.extend(result.states) + all_actions.extend(result.actions) + + self.mapleq.get_interaction_requests() + self.mapleq._learn_nsrts(self._trajs, 0, [] * len(self._trajs)) # pylint: disable=protected-access + return None diff --git a/predicators/approaches/maple_q_approach.py b/predicators/approaches/maple_q_approach.py index 44bd49b4d8..2a3b08835f 100644 --- a/predicators/approaches/maple_q_approach.py +++ b/predicators/approaches/maple_q_approach.py @@ -61,14 +61,19 @@ def __init__(self, initial_predicates: Set[Predicate], def get_name(cls) -> str: return "maple_q" - def _solve(self, task: Task, timeout: int) -> Callable[[State], Action]: + # pylint: disable=arguments-differ + def _solve(self, + task: Task, + timeout: int, + train_or_test: str = "") -> Callable[[State], Action]: def _option_policy(state: State) -> _Option: return self._q_function.get_option( state, task.goal, num_samples_per_ground_nsrt=CFG. - active_sampler_learning_num_samples) + active_sampler_learning_num_samples, + train_or_test=train_or_test) return utils.option_policy_to_policy( _option_policy, max_option_steps=CFG.max_num_steps_option_rollout) @@ -108,11 +113,15 @@ def _learn_nsrts(self, trajectories: List[LowLevelTrajectory], annotations: Optional[List[Any]]) -> None: # Start by learning NSRTs in the usual way. super()._learn_nsrts(trajectories, online_learning_cycle, annotations) - # Check the assumption that operators and options are 1:1. - # This is just an implementation convenience. - assert len({nsrt.option for nsrt in self._nsrts}) == len(self._nsrts) - for nsrt in self._nsrts: - assert nsrt.option_vars == nsrt.parameters + if CFG.approach == "active_sampler_learning": + # Check the assumption that operators and options are 1:1. + # This is just an implementation convenience. + assert len({ + nsrt.option # pragma: no cover. + for nsrt in self._nsrts + }) == len(self._nsrts) # pragma: no cover. + for nsrt in self._nsrts: # pragma: no cover. + assert nsrt.option_vars == nsrt.parameters # pragma: no cover. # On the first cycle, we need to register the ground NSRTs, goals, and # objects in the Q function so that it can define its inputs. if not online_learning_cycle: diff --git a/predicators/envs/grid_row.py b/predicators/envs/grid_row.py index 11ac2214a8..c2090b2ce7 100644 --- a/predicators/envs/grid_row.py +++ b/predicators/envs/grid_row.py @@ -3,6 +3,7 @@ from typing import List, Optional, Sequence, Set import matplotlib +import matplotlib.pyplot as plt import numpy as np from gym.spaces import Box @@ -179,6 +180,12 @@ def _Adjacent_holds(self, state: State, objects: Sequence[Object]) -> bool: class GridRowDoorEnv(GridRowEnv): """Simple variant on GridRow where there is also a door.""" + # Properties for rendering + cell_width = 1 + robot_width = 0.5 + robot_height = 0.75 + light_width = 0.25 + def __init__(self, use_gui: bool = True) -> None: super().__init__(use_gui) # type door with features ['x', and 'open'] @@ -193,6 +200,69 @@ def __init__(self, use_gui: bool = True) -> None: def get_name(cls) -> str: return "grid_row_door" + def render_state_plt( + self, + state: State, + task: EnvironmentTask, + action: Optional[Action] = None, + caption: Optional[str] = None) -> \ + matplotlib.figure.Figure: + fig, ax = plt.subplots(1, 1) + plt.xlim([0, len(self._cells)]) + plt.ylim([0, 1]) + + # Draw the cells. + for i in range(len(self._cells)): + rect = plt.Rectangle((i, 0), + self.cell_width, + self.cell_width, + edgecolor="gray", + facecolor="gray") + ax.add_patch(rect) + + # Draw light, door, and robot. + if self._LightOn_holds(state, (self._light, )): + light = plt.Rectangle( + (len(self._cells) - (self.cell_width + \ + self.light_width) / 2.0, + 1 - self.light_width), + self.light_width, + self.light_width, + edgecolor="yellow", + facecolor="yellow") + else: + light = plt.Rectangle( + (len(self._cells) - (self.cell_width + \ + self.light_width) / 2.0, + 1 - self.light_width), + self.light_width, + self.light_width, + edgecolor="white", + facecolor="white") + ax.add_patch(light) + door_pos = state.get(self._door, "x") + if state.get(self._door, "open"): + door = plt.Rectangle((door_pos - self.cell_width / 2.0, 0), + self.cell_width, + self.cell_width, + edgecolor="gray", + facecolor="gray") + else: + door = plt.Rectangle((door_pos - self.cell_width / 2.0, 0), + self.cell_width, + self.cell_width, + edgecolor="darkgoldenrod", + facecolor="darkgoldenrod") + ax.add_patch(door) + robot_pos = state.get(self._robot, "x") + robot = plt.Rectangle((robot_pos - self.robot_width / 2.0, 0), + self.robot_width, + self.robot_height, + edgecolor="red", + facecolor="red") + ax.add_patch(robot) + return fig + @property def predicates(self) -> Set[Predicate]: return { @@ -216,20 +286,21 @@ def _get_tasks(self, num: int, rng: np.random.Generator) -> List[EnvironmentTask]: # There is only one goal in this environment: to turn the light on. goal = {GroundAtom(self._LightOn, [self._light])} - # The only variation in the initial state is the light target level. tasks: List[EnvironmentTask] = [] while len(tasks) < num: state_dict = { self._robot: { "x": 0.5, }, + # Note: light level and door locations are fixed for now + # in order to maintain consistency across train and test self._light: { "x": len(self._cells) - 0.5, "level": 0.0, - "target": rng.uniform(0.5, 1.0), + "target": 0.75, }, self._door: { - "x": rng.choice(range(2, len(self._cells))) - 0.5, + "x": len(self._cells) // 2 + 0.5, "open": 0.0 } } diff --git a/predicators/ml_models.py b/predicators/ml_models.py index fecaabf767..2107a84999 100644 --- a/predicators/ml_models.py +++ b/predicators/ml_models.py @@ -1331,7 +1331,7 @@ def __init__(self, use_torch_gpu: bool = False, train_print_every: int = 1000, n_iter_no_change: int = 10000000, - discount: float = 0.99, + discount: float = 0.8, num_lookahead_samples: int = 5, replay_buffer_max_size: int = 1000000, replay_buffer_sample_with_replacement: bool = True) -> None: @@ -1354,6 +1354,12 @@ def __init__(self, self._num_ground_nsrts = 0 self._replay_buffer: Deque[MapleQData] = deque( maxlen=self._replay_buffer_max_size) + self._epsilon = CFG.active_sampler_learning_exploration_epsilon + self._min_epsilon = CFG.min_epsilon + self._use_epsilon_annealing = CFG.use_epsilon_annealing + self._ep_reduction = 10*(self._epsilon-self._min_epsilon) \ + /(CFG.num_online_learning_cycles*CFG.max_num_steps_interaction_request \ + *CFG.interactive_num_requests_per_cycle) def set_grounding(self, objects: Set[Object], goals: Collection[Set[GroundAtom]], @@ -1375,9 +1381,12 @@ def get_option(self, state: State, goal: Set[GroundAtom], num_samples_per_ground_nsrt: int, - epsilon: float = 0.0) -> _Option: + train_or_test: str = "test") -> _Option: """Get the best option under Q, epsilon-greedy.""" # Return a random option. + epsilon = self._epsilon + if train_or_test == "test": + epsilon = 0.0 if self._rng.uniform() < epsilon: options = self._sample_applicable_options_from_state( state, num_samples_per_applicable_nsrt=1) @@ -1391,8 +1400,16 @@ def get_option(self, self.predict_q_value(state, goal, option) for option in options ] idx = np.argmax(scores) + # Decay epsilon + if self._use_epsilon_annealing: + self.decay_epsilon() return options[idx] + def decay_epsilon(self) -> None: + """Decay epsilon for eps annealing.""" + self._epsilon = max(self._epsilon - self._ep_reduction, + self._min_epsilon) + def add_datum_to_replay_buffer(self, datum: MapleQData) -> None: """Add one datapoint to the replay buffer. @@ -1430,9 +1447,11 @@ def train_q_function(self) -> None: # We want to pick a total of num_lookahead_samples samples. while len(next_option_vecs) < self._num_lookahead_samples: # Sample 1 per NSRT until we reach the target number. - for option in self._sample_applicable_options_from_state( + for next_option in \ + self._sample_applicable_options_from_state( next_state): - next_option_vecs.append(self._vectorize_option(option)) + next_option_vecs.append( + self._vectorize_option(next_option)) for next_action_vec in next_option_vecs: x_hat = np.concatenate([ vectorized_next_state, vectorized_goal, next_action_vec diff --git a/predicators/settings.py b/predicators/settings.py index be81264d02..d0a77cdd30 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -578,6 +578,10 @@ class GlobalSettings: active_sampler_learning_replay_buffer_size = 1000000 active_sampler_learning_batch_size = 64 + # maple q function parameters + use_epsilon_annealing = True + min_epsilon = 0.05 + # skill competence model parameters skill_competence_model = "optimistic" skill_competence_model_num_em_iters = 3 diff --git a/tests/approaches/test_bridge_policy_approach.py b/tests/approaches/test_bridge_policy_approach.py index 39acebf35b..bc64f212ea 100644 --- a/tests/approaches/test_bridge_policy_approach.py +++ b/tests/approaches/test_bridge_policy_approach.py @@ -10,7 +10,8 @@ import predicators.teacher from predicators import utils from predicators.approaches import ApproachFailure, ApproachTimeout -from predicators.approaches.bridge_policy_approach import BridgePolicyApproach +from predicators.approaches.bridge_policy_approach import \ + BridgePolicyApproach, RLBridgePolicyApproach from predicators.bridge_policies import BridgePolicyDone from predicators.cogman import CogMan from predicators.envs import get_or_create_env @@ -233,3 +234,54 @@ def _mock_human_demonstratory_policy(*args, **kwargs): task.goal_holds, max_num_steps=CFG.horizon) assert task.goal_holds(traj.states[-1]) + + +def test_rl_bridge_policy_approach(): + """Tests for RLBridgePolicyApproach class.""" + # Test oracle bridge policy in grid_row_door. + args = { + "approach": "rl_bridge_policy", + "env": "grid_row_door", + "num_train_tasks": 1, + "num_test_tasks": 1, + "grid_row_num_cells": 3, + "strips_learner": "oracle", + "sampler_learner": "oracle", + "explorer": "maple_q", + "interactive_num_requests_per_cycle": 10, + "online_nsrt_learning_requests_per_cycle": 10, + "max_initial_demos": 0 + } + utils.reset_config(args) + env = get_or_create_env(CFG.env) + train_tasks = [t.task for t in env.get_train_tasks()] + approach = RLBridgePolicyApproach(env.predicates, + get_gt_options(env.get_name()), + env.types, env.action_space, train_tasks) + assert approach.get_name() == "rl_bridge_policy" + assert approach.is_learning_based + perceiver = create_perceiver("trivial") + exec_monitor = create_execution_monitor("trivial") + cogman = CogMan(approach, perceiver, exec_monitor) + num_online_transitions = 0 + total_query_cost = 0 + # Run online interaction once. + interaction_requests = cogman.get_interaction_requests() + interaction_results, query_cost = _generate_interaction_results( + cogman, env, teacher=None, requests=interaction_requests, cycle_num=0) + num_online_transitions += sum( + len(result.actions) for result in interaction_results) + total_query_cost += query_cost + # Learn from online interaction results, unless we are loading + # and not restarting learning. + if not CFG.load_approach or CFG.restart_learning: + cogman.learn_from_interaction_results(interaction_results) + # We should be adding more data to replay buffer + assert len(approach.mapleq._q_function._replay_buffer) > 0 # pylint: disable=protected-access + # Test that reward is positive for some trial + gets_reward = False + for (_, _, _, _, reward, _) in approach.mapleq._q_function._replay_buffer: # pylint: disable=protected-access + if reward > 0: + gets_reward = True + assert gets_reward + assert approach.learn_from_interaction_results([]) is None diff --git a/tests/approaches/test_maple_q_approach.py b/tests/approaches/test_maple_q_approach.py index 2200566f5d..17c2ed769b 100644 --- a/tests/approaches/test_maple_q_approach.py +++ b/tests/approaches/test_maple_q_approach.py @@ -51,6 +51,7 @@ def test_maple_q_approach(cover_num_blocks, cover_num_targets): approach = MapleQApproach(env.predicates, options, env.types, env.action_space, train_tasks) assert approach.is_learning_based + assert approach.get_name() == "maple_q" approach.learn_from_offline_dataset(Dataset([])) approach.load(online_learning_cycle=None) interaction_requests = approach.get_interaction_requests() diff --git a/tests/envs/test_grid_row.py b/tests/envs/test_grid_row.py index 19b1214a67..805d22ad4c 100644 --- a/tests/envs/test_grid_row.py +++ b/tests/envs/test_grid_row.py @@ -4,8 +4,10 @@ import pytest from predicators import utils -from predicators.envs.grid_row import GridRowDoorEnv, GridRowEnv +from predicators.envs import get_or_create_env +from predicators.envs.grid_row import GridRowEnv from predicators.ground_truth_models import get_gt_nsrts, get_gt_options +from predicators.settings import CFG def test_grid_row(): @@ -141,10 +143,10 @@ def test_grid_row_door(): utils.reset_config({ "env": "grid_row_door", "num_train_tasks": 1, - "num_test_tasks": 2, + "num_test_tasks": 1, "grid_row_num_cells": 10, }) - env = GridRowDoorEnv() + env = get_or_create_env(CFG.env) assert env.get_name() == "grid_row_door" for env_task in env.get_train_tasks(): task = env_task.task @@ -178,12 +180,9 @@ def test_grid_row_door(): env_train_tasks = env.get_train_tasks() assert len(env_train_tasks) == 1 env_test_tasks = env.get_test_tasks() - assert len(env_test_tasks) == 2 - env_task = env_test_tasks[1] - env.reset("test", 1) - with pytest.raises(NotImplementedError): - env.render(caption="Test") - + assert len(env_test_tasks) == 1 + env_task = env_test_tasks[0] + env.reset("test", 0) # Test NSRTs. MoveRobot, OpenDoor, TurnOffLight, TurnOnLight = sorted(nsrts) assert OpenDoor.name == "OpenDoor" @@ -219,6 +218,7 @@ def test_grid_row_door(): assert option.terminal(state) assert all(a.holds(state) for a in ground_nsrt.add_effects) assert not any(a.holds(state) for a in ground_nsrt.delete_effects) + assert isinstance(env.render_state(state, task), list) # Now repeatedly turn on the light until it succeeds. ground_nsrt = TurnOnLight.ground([robot, cell_order[-1], light]) for _ in range(100): @@ -230,6 +230,7 @@ def test_grid_row_door(): assert option.terminal(state) if all(a.holds(state) for a in ground_nsrt.add_effects): break + assert isinstance(env.render_state(state, task), list) assert all(a.holds(state) for a in ground_nsrt.add_effects) assert not any(a.holds(state) for a in ground_nsrt.delete_effects) # Now repeatedly turn off the light until it succeeds. @@ -243,5 +244,6 @@ def test_grid_row_door(): assert option.terminal(state) if all(a.holds(state) for a in ground_nsrt.add_effects): break + assert isinstance(env.render_state(state, task), list) assert all(a.holds(state) for a in ground_nsrt.add_effects) assert not any(a.holds(state) for a in ground_nsrt.delete_effects) diff --git a/tests/test_ml_models.py b/tests/test_ml_models.py index 7ea10d52de..4727a1e864 100644 --- a/tests/test_ml_models.py +++ b/tests/test_ml_models.py @@ -475,10 +475,12 @@ def test_maple_q_function(): # Test grounding. model.set_grounding(objects, [task.goal], ground_nsrts) # Test getting a random option. - sampled_option = model.get_option(task.init, task.goal, 1, epsilon=1.0) + sampled_option = model.get_option(task.init, task.goal, 1, \ + train_or_test="test") assert sampled_option.initiable(task.init) # Test getting a non-random option. - sampled_option = model.get_option(task.init, task.goal, 1, epsilon=0.0) + sampled_option = model.get_option(task.init, task.goal, 1, \ + train_or_test="test") assert sampled_option.initiable(task.init) # Test learning. data = (task.init, task.goal, option, task.init, 1.0, False) From 97b9b23e052f2a04492634270e769739ba6a7418 Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Sun, 16 Jun 2024 21:46:32 -0400 Subject: [PATCH 13/38] Enable predicate invention with both geometric and VLM predicates (#1666) * Intial commit. * Get State.allclose to work with non-None simulator state. * Create a ground atom dataset with both geometric and VLM predicates. * Improve implementation for State.allclose when simulator state is not None. * Run autoformatter. * Get linter to pass. * Get more tests to pass. * Get more tests to pass. * Get test_main.py to run without issue. * Add test for geo+vlm grammar_search offline data method. * fix coverage --------- Co-authored-by: Nishanth Kumar --- .../approaches/bridge_policy_approach.py | 2 +- .../grammar_search_invention_approach.py | 15 +++- predicators/datasets/__init__.py | 12 ++- predicators/envs/base_env.py | 10 +++ predicators/envs/burger.py | 4 + predicators/main.py | 11 ++- predicators/settings.py | 3 + predicators/structs.py | 9 ++- predicators/utils.py | 30 ++++++- .../test_grammar_search_invention_approach.py | 40 ++++++++++ tests/envs/test_burger.py | 1 + tests/test_structs.py | 10 +++ tests/test_utils.py | 80 +++++++++++++++++-- 13 files changed, 209 insertions(+), 18 deletions(-) diff --git a/predicators/approaches/bridge_policy_approach.py b/predicators/approaches/bridge_policy_approach.py index 09f06e9ec9..cc44f463a1 100644 --- a/predicators/approaches/bridge_policy_approach.py +++ b/predicators/approaches/bridge_policy_approach.py @@ -288,7 +288,7 @@ def learn_from_interaction_results( states = [traj.states[0]] atoms = atom_traj else: - states = utils.segment_trajectory_to_state_sequence( + states = utils.segment_trajectory_to_start_end_state_sequence( segmented_traj) atoms = utils.segment_trajectory_to_atoms_sequence( segmented_traj) diff --git a/predicators/approaches/grammar_search_invention_approach.py b/predicators/approaches/grammar_search_invention_approach.py index cbcecbc0b0..0b6d0b6439 100644 --- a/predicators/approaches/grammar_search_invention_approach.py +++ b/predicators/approaches/grammar_search_invention_approach.py @@ -790,7 +790,7 @@ def __post_init__(self) -> None: for traj in self.dataset.trajectories: # The init_atoms and final_atoms are not used. seg_traj = segment_trajectory(traj, predicates=set()) - state_seq = utils.segment_trajectory_to_state_sequence( + state_seq = utils.segment_trajectory_to_start_end_state_sequence( # pylint:disable=line-too-long seg_traj) self._state_sequences.append(state_seq) @@ -996,7 +996,18 @@ def _parse_atom_dataset_from_annotated_dataset( return (atom_dataset, candidates) def learn_from_offline_dataset(self, dataset: Dataset) -> None: - if not CFG.offline_data_method in [ + if CFG.offline_data_method in [ + "geo_and_demo_with_vlm_imgs", "geo_and_demo+labelled_atoms", + "geo_and_saved_vlm_img_demos_folder" + ]: + atom_dataset_from_grammar, candidates_from_grammar = \ + self._generate_atom_dataset_via_grammar(dataset) + atom_dataset_from_vlm, candidates_from_vlm = \ + self._parse_atom_dataset_from_annotated_dataset(dataset) + atom_dataset = utils.merge_ground_atom_datasets( + atom_dataset_from_grammar, atom_dataset_from_vlm) + candidates = candidates_from_grammar | candidates_from_vlm + elif not CFG.offline_data_method in [ "demo+labelled_atoms", "saved_vlm_img_demos_folder", "demo_with_vlm_imgs" ]: diff --git a/predicators/datasets/__init__.py b/predicators/datasets/__init__.py index 657d5ee471..220259d737 100644 --- a/predicators/datasets/__init__.py +++ b/predicators/datasets/__init__.py @@ -45,7 +45,9 @@ def create_dataset(env: BaseEnv, train_tasks: List[Task], n = int(CFG.teacher_dataset_num_examples) assert n >= 1, "Must have at least 1 example of each predicate" return create_ground_atom_data(env, base_dataset, excluded_preds, n) - if CFG.offline_data_method == "demo_with_vlm_imgs": # pragma: no cover # pylint:disable=line-too-long + if CFG.offline_data_method in [ + "demo_with_vlm_imgs", "geo_and_demo_with_vlm_imgs" + ]: # pragma: no cover. # NOTE: this below method is tested separately; it's just that testing # it by calling the above function is painful because a VLM is # instantiated and called from inside this method, but when testing, @@ -71,10 +73,14 @@ def create_dataset(env: BaseEnv, train_tasks: List[Task], # a VLM. return create_ground_atom_data_from_generated_demos( demo_data, env, known_predicates, train_tasks) - if CFG.offline_data_method == "demo+labelled_atoms": + if CFG.offline_data_method in [ + "demo+labelled_atoms", "geo_and_demo+labelled_atoms" + ]: return create_ground_atom_data_from_labelled_txt( env, train_tasks, known_options) - if CFG.offline_data_method == "saved_vlm_img_demos_folder": # pragma: no cover # pylint:disable=line-too-long + if CFG.offline_data_method in [ + "saved_vlm_img_demos_folder", "geo_and_saved_vlm_img_demos_folder" + ]: # pragma: no cover. # NOTE: this below method is tested separately; it's just that testing # it by calling the above function is painful because a VLM is # instantiated and called from inside this method, but when testing, diff --git a/predicators/envs/base_env.py b/predicators/envs/base_env.py index bcd0994338..b9e3c31032 100644 --- a/predicators/envs/base_env.py +++ b/predicators/envs/base_env.py @@ -76,6 +76,16 @@ def goal_predicates(self) -> Set[Predicate]: """Get the subset of self.predicates that are used in goals.""" raise NotImplementedError("Override me!") + @property + def agent_goal_predicates(self) -> Set[Predicate]: + """Get the goal predicates that we want the agent to use, which may be + different than the ones the demonstrator uses. + + This is used when inventing VLM predicates. Unless overridden, + these are the same as the original goal predicates. + """ + return self.goal_predicates + @property @abc.abstractmethod def types(self) -> Set[Type]: diff --git a/predicators/envs/burger.py b/predicators/envs/burger.py index 37dc163062..61a70d9772 100644 --- a/predicators/envs/burger.py +++ b/predicators/envs/burger.py @@ -348,6 +348,10 @@ def goal_predicates(self) -> Set[Predicate]: return {self._On, self._IsCooked, self._IsSliced} # return {self._On, self._GoalHack} + @property + def agent_goal_predicates(self) -> Set[Predicate]: + return {self._On, self._GoalHack} + @property def action_space(self) -> Box: # dx (column), dy (row), direction, cut/cook, pick/place diff --git a/predicators/main.py b/predicators/main.py index 7b3cd4657c..4e4ad8265f 100644 --- a/predicators/main.py +++ b/predicators/main.py @@ -95,7 +95,16 @@ def main() -> None: # is often created during env __init__(). env.action_space.seed(CFG.seed) assert env.goal_predicates.issubset(env.predicates) - preds, _ = utils.parse_config_excluded_predicates(env) + included_preds, excluded_preds = utils.parse_config_excluded_predicates( + env) + # The known predicates are passed into the approach and into dataset + # creation. In some cases, like when inventing geometric and VLM predicates, + # we want to hide certain goal predicates from the agent by replacing them + # with agent-specific goal predicates that the environment defines. + # Note that inside dataset creation, the known predicates are only used to + # create a VLM dataset, so we can just overwrite the variable `preds`. + preds = utils.replace_goals_with_agent_specific_goals( + included_preds, excluded_preds, env) # Create the train tasks. env_train_tasks = env.get_train_tasks() # We assume that a train Task can be constructed from a EnvironmentTask. diff --git a/predicators/settings.py b/predicators/settings.py index d0a77cdd30..4a15f39a10 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -51,6 +51,9 @@ class GlobalSettings: # used by VLM predicate invention, where we want to invent goal predicates # and different task goals are provided to the agent and the demonstrator. allow_exclude_goal_predicates = False + # Normally, State.allclose() raises an error if the simulator state of + # either of its arguments is not None. + allow_state_allclose_comparison_despite_simulator_state = False # cover_multistep_options env parameters cover_multistep_action_limits = [-np.inf, np.inf] diff --git a/predicators/structs.py b/predicators/structs.py index b77f2ce353..cf703f2e3f 100644 --- a/predicators/structs.py +++ b/predicators/structs.py @@ -171,9 +171,12 @@ def allclose(self, other: State) -> bool: """Return whether this state is close enough to another one, i.e., its objects are the same, and the features are close.""" if self.simulator_state is not None or \ - other.simulator_state is not None: - raise NotImplementedError("Cannot use allclose when " - "simulator_state is not None.") + other.simulator_state is not None: + if not CFG.allow_state_allclose_comparison_despite_simulator_state: + raise NotImplementedError("Cannot use allclose when " + "simulator_state is not None.") + if self.simulator_state != other.simulator_state: + return False if not sorted(self.data) == sorted(other.data): return False for obj in self.data: diff --git a/predicators/utils.py b/predicators/utils.py index bcbd7d91e6..f503c8fc89 100644 --- a/predicators/utils.py +++ b/predicators/utils.py @@ -149,7 +149,7 @@ def count_branching_factor(strips_ops: List[STRIPSOperator], return total_branching_factor -def segment_trajectory_to_state_sequence( +def segment_trajectory_to_start_end_state_sequence( seg_traj: List[Segment]) -> List[State]: """Convert a trajectory of segments into a trajectory of states, made up of only the initial/final states of the segments. @@ -2715,6 +2715,24 @@ def save_ground_atom_dataset(ground_atom_dataset: List[GroundAtomTrajectory], pkl.dump(ground_atom_dataset_to_pkl, f) +def merge_ground_atom_datasets( + gad1: List[GroundAtomTrajectory], + gad2: List[GroundAtomTrajectory]) -> List[GroundAtomTrajectory]: + """Merges two ground atom datasets sharing the same underlying low-level + trajectory via the union of ground atoms at each state.""" + assert len(gad1) == len( + gad2), "Ground atom datasets must be of the same length to merge them." + merged_ground_atom_dataset = [] + for ground_atom_traj1, ground_atom_traj2 in zip(gad1, gad2): + ll_traj1, ga_list1 = ground_atom_traj1 + ll_traj2, ga_list2 = ground_atom_traj2 + assert ll_traj1 == ll_traj2, "Ground atom trajectories must share " \ + "the same low-level trajectory to be able to merge them." + merged_ga_list = [ga1 | ga2 for ga1, ga2 in zip(ga_list1, ga_list2)] + merged_ground_atom_dataset.append((ll_traj1, merged_ga_list)) + return merged_ground_atom_dataset + + def extract_preds_and_types( ops: Collection[NSRTOrSTRIPSOperator] ) -> Tuple[Dict[str, Predicate], Dict[str, Type]]: @@ -3511,6 +3529,16 @@ def parse_config_excluded_predicates( return included, excluded +def replace_goals_with_agent_specific_goals( + included_predicates: Set[Predicate], + excluded_predicates: Set[Predicate], env: BaseEnv) -> Set[Predicate]: + """Replace original goal predicates with agent-specific goal predicates if + the environment defines them.""" + preds = included_predicates - env.goal_predicates \ + | env.agent_goal_predicates - excluded_predicates + return preds + + def null_sampler(state: State, goal: Set[GroundAtom], rng: np.random.Generator, objs: Sequence[Object]) -> Array: """A sampler for an NSRT with no continuous parameters.""" diff --git a/tests/approaches/test_grammar_search_invention_approach.py b/tests/approaches/test_grammar_search_invention_approach.py index 1c1bdba500..140742b206 100644 --- a/tests/approaches/test_grammar_search_invention_approach.py +++ b/tests/approaches/test_grammar_search_invention_approach.py @@ -201,6 +201,46 @@ def test_invention_from_txt_file(): assert approach._get_current_predicates() == env.goal_predicates # pylint:disable=protected-access +def test_geo_and_vlm_invention(): + """Test constructing an atom dataset with both geo and vlm predicates.""" + utils.reset_config({ + "env": + "ice_tea_making", + "num_train_tasks": + 1, + "num_test_tasks": + 0, + "offline_data_method": + "geo_and_demo+labelled_atoms", + "data_dir": + "tests/datasets/mock_vlm_datasets", + "handmade_demo_filename": + "ice_tea_making__demo+labelled_atoms__manual__1.txt" + }) + env = IceTeaMakingEnv() + train_tasks = env.get_train_tasks() + predicates, _ = utils.parse_config_excluded_predicates(env) + loaded_dataset = create_dataset(env, train_tasks, + get_gt_options(env.get_name()), predicates) + approach = GrammarSearchInventionApproach(env.goal_predicates, + get_gt_options(env.get_name()), + env.types, env.action_space, + train_tasks) + approach.learn_from_offline_dataset(loaded_dataset) + # The ice_tea_making__demo+labelled_atoms__manual__1.txt happens to + # set all atoms to True at all timesteps, and so we expect predicate + # invention to not select any of the predicates (only select the goal) + # predicates. + # If you investigate the atom_dataset created inside + # learn_from_offline_dataset() you'll see some grammar-based predicates + # invented that are based on the DummyGoal, but they don't get selected. + # A better test would alter the dataset such that some grammar-based + # predicates actually get selected, so we can verify that geo + vlm + # predicate invention works more explicitly. + assert len(approach._get_current_predicates()) == 1 # pylint:disable=protected-access + assert approach._get_current_predicates() == env.goal_predicates # pylint:disable=protected-access + + def test_euclidean_grammar(): """Tests for the EuclideanGrammar.""" utils.reset_config({"env": "stick_button_move"}) diff --git a/tests/envs/test_burger.py b/tests/envs/test_burger.py index 4c45ffd35a..30f91aa949 100644 --- a/tests/envs/test_burger.py +++ b/tests/envs/test_burger.py @@ -32,6 +32,7 @@ def test_burger(): assert len(obj.type.feature_names) == len(task.init[obj]) assert len(env.predicates) == 12 assert len(env.goal_predicates) == 3 + assert len(env.agent_goal_predicates) == 2 assert env.get_name() == "burger" assert len(env.types) == 11 options = get_gt_options(env.get_name()) diff --git a/tests/test_structs.py b/tests/test_structs.py index e1181d000b..9dc2084b56 100644 --- a/tests/test_structs.py +++ b/tests/test_structs.py @@ -184,6 +184,16 @@ def test_state(): state_with_sim = State({}, "simulator_state") assert state_with_sim.simulator_state == "simulator_state" assert state.simulator_state is None + + # Can use allclose with non-None simulator_state if explicitly allowed via + # settings. + utils.reset_config( + {"allow_state_allclose_comparison_despite_simulator_state": "True"}) + state5 = state4.copy() + assert state4.allclose(state5) + state5.simulator_state = "not dummy" + assert not state4.allclose(state5) + return state diff --git a/tests/test_utils.py b/tests/test_utils.py index 7fa10b9cb3..e951f070a8 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -103,7 +103,7 @@ def test_count_positives_for_ops(max_groundings, exp_num_true, exp_num_false): def test_segment_trajectory_to_state_and_atoms_sequence(): - """Tests for segment_trajectory_to_state_sequence() and + """Tests for segment_trajectory_to_start_end_state_sequence() and segment_trajectory_to_atoms_sequence().""" # Set up the segments. cup_type = Type("cup_type", ["feat1"]) @@ -125,21 +125,23 @@ def test_segment_trajectory_to_state_and_atoms_sequence(): final_atoms = {not_on([cup, plate])} segment1 = Segment(traj1, init_atoms, final_atoms) segment2 = Segment(traj2, final_atoms, init_atoms) - # Test segment_trajectory_to_state_sequence(). - state_seq = utils.segment_trajectory_to_state_sequence([segment1]) + # Test segment_trajectory_to_start_end_state_sequence(). + state_seq = utils.segment_trajectory_to_start_end_state_sequence( + [segment1]) assert state_seq == [state0, state2] - state_seq = utils.segment_trajectory_to_state_sequence( + state_seq = utils.segment_trajectory_to_start_end_state_sequence( [segment1, segment2]) assert state_seq == [state0, state2, state0] - state_seq = utils.segment_trajectory_to_state_sequence( + state_seq = utils.segment_trajectory_to_start_end_state_sequence( [segment1, segment2, segment1, segment2]) assert state_seq == [state0, state2, state0, state2, state0] with pytest.raises(AssertionError): # Need at least one segment in the trajectory. - utils.segment_trajectory_to_state_sequence([]) + utils.segment_trajectory_to_start_end_state_sequence([]) with pytest.raises(AssertionError): # Segments don't chain together correctly. - utils.segment_trajectory_to_state_sequence([segment1, segment1]) + utils.segment_trajectory_to_start_end_state_sequence( + [segment1, segment1]) # Test segment_trajectory_to_atoms_sequence(). atoms_seq = utils.segment_trajectory_to_atoms_sequence([segment1]) assert atoms_seq == [init_atoms, final_atoms] @@ -1949,6 +1951,70 @@ def test_create_ground_atom_dataset(): assert ground_atom_dataset[0][1][1] == {GroundAtom(on, [cup1, plate1])} +def test_merge_ground_atom_datasets(): + """Tests for merge_ground_atom_datasets().""" + utils.reset_config({ + "env": "test_env", + }) + cup_type = Type("cup_type", ["feat1", "color"]) + plate_type = Type("plate_type", ["feat1"]) + on = Predicate("On", [cup_type, plate_type], + lambda s, o: s.get(o[0], "feat1") > s.get(o[1], "feat1")) + dark = Predicate("Dark", [cup_type], + lambda s, o: s.get(o[0], "color") > 0.5) + cup1 = cup_type("cup1") + cup2 = cup_type("cup2") + plate1 = plate_type("plate1") + plate2 = plate_type("plate2") + states = [ + State({ + cup1: np.array([0.5, 0.2]), + cup2: np.array([0.1, 0.6]), + plate1: np.array([1.0]), + plate2: np.array([1.2]) + }), + State({ + cup1: np.array([1.1, 0.2]), + cup2: np.array([0.1, 0.6]), + plate1: np.array([1.0]), + plate2: np.array([1.2]) + }), + State({ + cup1: np.array([1.1, 0.7]), + cup2: np.array([0.1, 0.6]), + plate1: np.array([1.0]), + plate2: np.array([1.2]) + }) + ] + actions = [ + Action(np.array([0.0]), DummyOption), + Action(np.array([0.0]), DummyOption) + ] + dataset = [LowLevelTrajectory(states, actions)] + gad1 = utils.create_ground_atom_dataset(dataset, {on}) + gad2 = utils.create_ground_atom_dataset(dataset, {dark}) + ground_atom_dataset = utils.merge_ground_atom_datasets(gad1, gad2) + assert len(ground_atom_dataset) == 1 + assert len(ground_atom_dataset[0]) == 2 + assert len(ground_atom_dataset[0][0].states) == len(states) + assert all(gs.allclose(s) for gs, s in \ + zip(ground_atom_dataset[0][0].states, states)) + assert len(ground_atom_dataset[0][0].actions) == len(actions) + assert all(ga == a + for ga, a in zip(ground_atom_dataset[0][0].actions, actions)) + assert len(ground_atom_dataset[0][1]) == len(states) == 3 + assert ground_atom_dataset[0][1][0] == {GroundAtom(dark, [cup2])} + assert ground_atom_dataset[0][1][1] == { + GroundAtom(dark, [cup2]), + GroundAtom(on, [cup1, plate1]) + } + assert ground_atom_dataset[0][1][2] == { + GroundAtom(dark, [cup2]), + GroundAtom(on, [cup1, plate1]), + GroundAtom(dark, [cup1]) + } + + def test_get_reachable_atoms(): """Tests for get_reachable_atoms().""" cup_type = Type("cup_type", ["feat1"]) From 00c42d6817f661604165a6f7d97dceb4ed3d55a9 Mon Sep 17 00:00:00 2001 From: Alicia Li <102700848+matrixbalto@users.noreply.github.com> Date: Thu, 20 Jun 2024 10:35:05 -0400 Subject: [PATCH 14/38] Added CallPlanner action to RL_Bridge_Policy (#1670) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * callplanner * init nsrt * assertion * tab * minor fix * autoformat * fixes * small fix * minor change * more fixes * more fixes * comment --------- Co-authored-by: “matrixbalto” <“ilfangliu@gmail.com”> --- .../approaches/bridge_policy_approach.py | 173 +++++++++++++----- predicators/approaches/maple_q_approach.py | 4 +- predicators/explorers/maple_q_explorer.py | 4 +- predicators/ml_models.py | 2 + 4 files changed, 133 insertions(+), 50 deletions(-) diff --git a/predicators/approaches/bridge_policy_approach.py b/predicators/approaches/bridge_policy_approach.py index cc44f463a1..4a49f7a2b7 100644 --- a/predicators/approaches/bridge_policy_approach.py +++ b/predicators/approaches/bridge_policy_approach.py @@ -42,8 +42,9 @@ import logging import time -from typing import Callable, List, Optional, Sequence, Set +from typing import Callable, Dict, List, Optional, Sequence, Set +import numpy as np from gym.spaces import Box from predicators import utils @@ -54,11 +55,11 @@ from predicators.nsrt_learning.segmentation import segment_trajectory from predicators.option_model import _OptionModelBase from predicators.settings import CFG -from predicators.structs import NSRT, Action, BridgeDataset, DefaultState, \ - DemonstrationQuery, DemonstrationResponse, InteractionRequest, \ - InteractionResult, LowLevelTrajectory, ParameterizedOption, Predicate, \ - Query, State, Task, Type, _GroundNSRT, _Option -from predicators.utils import OptionExecutionFailure +from predicators.structs import NSRT, Action, Array, BridgeDataset, \ + DefaultState, DemonstrationQuery, DemonstrationResponse, \ + InteractionRequest, InteractionResult, LiftedAtom, LowLevelTrajectory, \ + Object, ParameterizedOption, Predicate, Query, State, Task, Type, \ + Variable, _GroundNSRT, _Option class BridgePolicyApproach(OracleApproach): @@ -129,7 +130,7 @@ def _policy(s: State) -> Action: except BridgePolicyDone: assert current_control == "bridge" failed_option = None # not used, but satisfy linting - except OptionExecutionFailure as e: + except utils.OptionExecutionFailure as e: failed_option = e.info["last_failed_option"] if failed_option is not None: all_failed_options.append(failed_option) @@ -180,7 +181,7 @@ def _policy(s: State) -> Action: ) try: return current_policy(s) - except OptionExecutionFailure as e: + except utils.OptionExecutionFailure as e: all_failed_options.append(e.info["last_failed_option"]) raise ApproachFailure( e.args[0], info={"all_failed_options": all_failed_options}) @@ -239,7 +240,7 @@ def _act_policy(s: State) -> Action: reached_stuck_state = True all_failed_options = e.info["all_failed_options"] # Approach failures not caught in interaction loop. - raise OptionExecutionFailure(e.args[0], e.info) + raise utils.OptionExecutionFailure(e.args[0], e.info) def _termination_fn(s: State) -> bool: return reached_stuck_state or task.goal_holds(s) @@ -365,10 +366,70 @@ def __init__(self, action_space, train_tasks, task_planning_heuristic, max_skeletons_optimized) self._maple_initialized = False + if task_planning_heuristic == "default": + task_planning_heuristic = CFG.sesame_task_planning_heuristic + self._task_planning_heuristic = task_planning_heuristic self._trajs: List[LowLevelTrajectory] = [] + self.CanPlan = Predicate("CanPlan", [], self._Can_plan) + self.CallPlanner = utils.SingletonParameterizedOption( + "CallPlanner", + types=None, + policy=self.call_planner_policy, + params_space=Box(low=np.array([]), high=np.array([]), shape=(0, )), + ) + initial_options.add(self.CallPlanner) + self._initial_options = initial_options self.mapleq=MapleQApproach(self._get_current_predicates(), \ self._initial_options, self._types, \ self._action_space, self._train_tasks) + self._current_control: Optional[str] = None + option_policy = self._get_option_policy_by_planning( + self._train_tasks[0], CFG.timeout) + self._current_policy = utils.option_policy_to_policy( + option_policy, + max_option_steps=CFG.max_num_steps_option_rollout, + raise_error_on_repeated_state=True, + ) + self._bridge_called_state = State(data={}) + self._policy_logs: List[Optional[str]] = [] + + def _Can_plan(self, state: State, _: Sequence[Object]) -> bool: + if (self.mapleq._q_function._vectorize_state(state) != # pylint: disable=protected-access + self.mapleq._q_function._vectorize_state( # pylint: disable=protected-access + self._bridge_called_state)).any(): # pylint: disable=protected-access + return True + return False + + def call_planner_policy(self, state: State, _: Dict, __: Sequence[Object], + ___: Array) -> Action: + """policy for CallPlanner option.""" + self._current_control = "planner" + # create a new task where the init state is our current state + current_task = Task(state, self._train_tasks[0].goal) + option_policy = self._get_option_policy_by_planning( + current_task, CFG.timeout) + self._current_policy = utils.option_policy_to_policy( + option_policy, + max_option_steps=CFG.max_num_steps_option_rollout, + raise_error_on_repeated_state=True, + ) + + return Action(np.array([0.0, 0.0, 0.0], dtype=np.float32)) + + def call_planner_nsrt(self) -> NSRT: + """CallPlanner NSRT.""" + parameters: Sequence[Variable] = [] + option_vars = parameters + option = self.CallPlanner + preconditions = {LiftedAtom(self.CanPlan, [])} + add_effects: Set[LiftedAtom] = set() + delete_effects: Set[LiftedAtom] = set() + + ignore_effects: Set[Predicate] = set() + call_planner_nsrt = NSRT("CallPlanner", parameters, preconditions, + add_effects, delete_effects, ignore_effects, + option, option_vars, utils.null_sampler) + return call_planner_nsrt @classmethod def get_name(cls) -> str: @@ -381,6 +442,8 @@ def is_learning_based(self) -> bool: def _init_nsrts(self) -> None: """Initializing nsrts for MAPLE Q.""" nsrts = self._get_current_nsrts() + callplanner_nsrt = self.call_planner_nsrt() + nsrts.add(callplanner_nsrt) predicates = self._get_current_predicates() all_ground_nsrts: Set[_GroundNSRT] = set() if CFG.sesame_grounder == "naive": @@ -410,48 +473,42 @@ def _solve(self, task: Task, timeout: int, train_or_test: str = "test") -> Callable[[State], Action]: - if not self._maple_initialized: - self.mapleq = MapleQApproach(self._get_current_predicates(), - self._initial_options, self._types, - self._action_space, self._train_tasks) - self._maple_initialized = True - self._init_nsrts() - # Start by planning. Note that we cannot start with the bridge policy # because the bridge policy takes as input the last failed NSRT. - current_control = "planner" + self._current_control = "planner" option_policy = self._get_option_policy_by_planning(task, timeout) - current_policy = utils.option_policy_to_policy( + self._current_policy = utils.option_policy_to_policy( option_policy, max_option_steps=CFG.max_num_steps_option_rollout, raise_error_on_repeated_state=True, ) - all_failed_options: List[_Option] = [] - - # Prevent infinite loops by detecting if the bridge policy is called - # twice with the same state. - last_bridge_policy_state = DefaultState + if not self._maple_initialized: + self.mapleq = MapleQApproach(self._get_current_predicates(), + self._initial_options, self._types, + self._action_space, self._train_tasks) + self._maple_initialized = True + self._init_nsrts() def _policy(s: State) -> Action: - nonlocal current_control, current_policy, last_bridge_policy_state - # Normal execution. Either keep executing the current option, or # switch to the next option if it has terminated. try: - action = current_policy(s) + action = self._current_policy(s) + if train_or_test == "train": + self._policy_logs.append(self._current_control) return action - except OptionExecutionFailure as e: - failed_option = e.info["last_failed_option"] - if failed_option is not None: - all_failed_options.append(failed_option) - + except utils.OptionExecutionFailure: + logging.debug(f"Failed control: {self._current_control}") # Switch control from planner to bridge. - assert current_control == "planner" - current_control = "bridge" - - current_policy = self.mapleq._solve( # pylint: disable=protected-access + assert self._current_control == "planner" + self._current_control = "bridge" + if train_or_test == "train": + self._policy_logs.append(self._current_control) + self._bridge_called_state = s + self._current_policy = self.mapleq._solve( # pylint: disable=protected-access task, timeout, train_or_test) - action = current_policy(s) + action = self._current_policy(s) + return action return _policy @@ -460,12 +517,20 @@ def _create_interaction_request(self, train_task_idx: int) -> InteractionRequest: task = self._train_tasks[train_task_idx] policy = self._solve(task, timeout=CFG.timeout, train_or_test="train") - - reached_stuck_state = False - all_failed_options = None + just_starting = True def _act_policy(s: State) -> Action: - nonlocal reached_stuck_state, all_failed_options + nonlocal just_starting + if just_starting: + self._current_control = "planner" + option_policy = self._get_option_policy_by_planning( + task, CFG.timeout) + self._current_policy = utils.option_policy_to_policy( + option_policy, + max_option_steps=CFG.max_num_steps_option_rollout, + raise_error_on_repeated_state=True, + ) + just_starting = False return policy(s) def _termination_fn(s: State) -> bool: @@ -484,15 +549,29 @@ def learn_from_interaction_results( # for efficiency. if not results: return None - all_states = [] - all_actions = [] - - for result in results: - new_traj = LowLevelTrajectory(result.states, result.actions) + policy_logs = self._policy_logs + for i in range(len(results)): + result = results[i] + policy_log = policy_logs[:len(result.states[:-1])] + # We index max(j - 1, 0) to count for the case when CallPlanner + # is used, since "planner" is added to the corresponding policy_log. + # When j = 0, planner is always in control + mapleq_states = [ + state for j, state in enumerate(result.states[:-1]) + if policy_log[j] == "bridge" + or policy_log[max(j - 1, 0)] == "bridge" + ] + mapleq_actions = [ + action for j, action in enumerate(result.actions) + if policy_log[j] == "bridge" + or policy_log[max(j - 1, 0)] == "bridge" + ] + mapleq_states.append(result.states[-1]) + new_traj = LowLevelTrajectory(mapleq_states, mapleq_actions) self._trajs.append(new_traj) - all_states.extend(result.states) - all_actions.extend(result.actions) + policy_logs = policy_logs[len(result.states) - 1:] self.mapleq.get_interaction_requests() self.mapleq._learn_nsrts(self._trajs, 0, [] * len(self._trajs)) # pylint: disable=protected-access + self._policy_logs = [] return None diff --git a/predicators/approaches/maple_q_approach.py b/predicators/approaches/maple_q_approach.py index 2a3b08835f..865a6d8b9b 100644 --- a/predicators/approaches/maple_q_approach.py +++ b/predicators/approaches/maple_q_approach.py @@ -124,7 +124,9 @@ def _learn_nsrts(self, trajectories: List[LowLevelTrajectory], assert nsrt.option_vars == nsrt.parameters # pragma: no cover. # On the first cycle, we need to register the ground NSRTs, goals, and # objects in the Q function so that it can define its inputs. - if not online_learning_cycle: + # Do not set grounding for rl_bridge_policy since it was set already + # in init_nsrts + if not online_learning_cycle and CFG.approach != "rl_bridge_policy": all_ground_nsrts: Set[_GroundNSRT] = set() if CFG.sesame_grounder == "naive": for nsrt in self._nsrts: diff --git a/predicators/explorers/maple_q_explorer.py b/predicators/explorers/maple_q_explorer.py index 8318ed5f78..4da4e08b08 100644 --- a/predicators/explorers/maple_q_explorer.py +++ b/predicators/explorers/maple_q_explorer.py @@ -38,12 +38,12 @@ def get_name(cls) -> str: def _get_exploration_strategy(self, train_task_idx: int, timeout: int) -> ExplorationStrategy: - eps = CFG.active_sampler_learning_exploration_epsilon num_samples = CFG.active_sampler_learning_num_samples goal = self._train_tasks[train_task_idx].goal def _option_policy(state: State) -> _Option: - return self._q_function.get_option(state, goal, num_samples, eps) + return self._q_function.get_option(state, \ + goal, num_samples, "train") policy = utils.option_policy_to_policy( _option_policy, max_option_steps=CFG.max_num_steps_option_rollout) diff --git a/predicators/ml_models.py b/predicators/ml_models.py index 2107a84999..5972e7a783 100644 --- a/predicators/ml_models.py +++ b/predicators/ml_models.py @@ -1392,6 +1392,8 @@ def get_option(self, state, num_samples_per_applicable_nsrt=1) # Note that this assumes that the output of sampling is completely # random, including in the order of ground NSRTs. + if self._use_epsilon_annealing: + self.decay_epsilon() return options[0] # Return the best option (approx argmax.) options = self._sample_applicable_options_from_state( From 90fa964f91c140085ea58d218f9bbadd025cf2ac Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Mon, 24 Jun 2024 16:31:39 -0400 Subject: [PATCH 15/38] Initial commit. (#1671) --- predicators/main.py | 31 ++++++++++++++++++++++--------- 1 file changed, 22 insertions(+), 9 deletions(-) diff --git a/predicators/main.py b/predicators/main.py index 4e4ad8265f..3d5680b8d6 100644 --- a/predicators/main.py +++ b/predicators/main.py @@ -99,12 +99,16 @@ def main() -> None: env) # The known predicates are passed into the approach and into dataset # creation. In some cases, like when inventing geometric and VLM predicates, - # we want to hide certain goal predicates from the agent by replacing them - # with agent-specific goal predicates that the environment defines. - # Note that inside dataset creation, the known predicates are only used to - # create a VLM dataset, so we can just overwrite the variable `preds`. + # we want to hide certain goal predicates from the agent because we may + # want to invent them. So we can replace them with agent-specific goal + # predicates that the environment defines. Note that inside dataset + # creation, the known predicates are only used to create a VLM dataset, so + # we can just overwrite the variable `preds`. No replacing is done if the + # approach is oracle because the ground truth operators are defined in terms + # of the original goal predicates. preds = utils.replace_goals_with_agent_specific_goals( - included_preds, excluded_preds, env) + included_preds, excluded_preds, + env) if CFG.approach != "oracle" else included_preds # Create the train tasks. env_train_tasks = env.get_train_tasks() # We assume that a train Task can be constructed from a EnvironmentTask. @@ -125,7 +129,9 @@ def main() -> None: # using predicates that differ from those in the goals of the tasks that the # demonstrator solves, then replace those predicates accordingly. This is # used in VLM predicate invention where we want to invent certain goal - # predicates that the demonstrator needed to solve the task. + # predicates that the demonstrator needed to solve the task. We don't need + # worry about not doing this replacing if the approach is oracle because the + # "unedited" train tasks are passed into offline dataset creation. approach_train_tasks = [ task.replace_goal_with_alt_goal() for task in stripped_train_tasks ] @@ -326,9 +332,16 @@ def _generate_interaction_results( def _run_testing(env: BaseEnv, cogman: CogMan) -> Metrics: - test_tasks = [ - task.replace_goal_with_alt_goal() for task in env.get_test_tasks() - ] + # If the goals of the tasks that the approaches solve need to be described + # using predicates that differ from those in the goals of the tasks that the + # demonstrator solves, then replace those predicates accordingly. This is + # used in VLM predicate invention where we want to invent certain goal + # predicates that the demonstrator needed to solve the task. No replacing is + # done if the approach is oracle because the ground truth operators are + # defined in terms of the original goal predicates. + test_tasks = env.get_test_tasks() + if CFG.approach != "oracle": + test_tasks = [task.replace_goal_with_alt_goal() for task in test_tasks] num_found_policy = 0 num_solved = 0 cogman.reset_metrics() From 432ec18ee47e0f42149d013d02d2bbdae944ba72 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Tue, 25 Jun 2024 08:30:58 -0700 Subject: [PATCH 16/38] done (#1673) --- predicators/planning.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/predicators/planning.py b/predicators/planning.py index 5160eedf21..123323ff0a 100644 --- a/predicators/planning.py +++ b/predicators/planning.py @@ -985,7 +985,15 @@ def generate_sas_file_for_fd( # Run to generate sas cmd_str = (f"{timeout_cmd} {timeout} {exec_str} {alias_flag} " f"--sas-file {sas_file} {dom_file} {prob_file}") - subprocess.getoutput(cmd_str) + fd_translation_cmd_output = subprocess.getoutput(cmd_str) + if "Driver aborting" in fd_translation_cmd_output: + logging.debug(fd_translation_cmd_output) + logging.debug(prob_str) + raise PlanningFailure("FD failed to translate PDDL " + "to sas, there is likely a " + "dr-reachability issue! Run " + "with '--debug' flag to see the " + "output from FD.") return sas_file From f3b9bf125fe8c1d861c678010626d3b6570c47d4 Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Tue, 25 Jun 2024 11:41:55 -0400 Subject: [PATCH 17/38] Fix geo and VLM predicate invention's use of annotations during nsrt learning. (#1672) * Initial commit. * Add geo and vlm approaches to offline data methods considered in _learn_nsrts() for annotations. * Remove pdb statement. * Run yapf. --------- Co-authored-by: Nishanth Kumar --- predicators/approaches/nsrt_learning_approach.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/predicators/approaches/nsrt_learning_approach.py b/predicators/approaches/nsrt_learning_approach.py index d6dcbf06ff..240604abaf 100644 --- a/predicators/approaches/nsrt_learning_approach.py +++ b/predicators/approaches/nsrt_learning_approach.py @@ -76,7 +76,9 @@ def _learn_nsrts(self, trajectories: List[LowLevelTrajectory], utils.save_ground_atom_dataset(ground_atom_dataset, dataset_fname) elif CFG.offline_data_method in [ "demo+labelled_atoms", "saved_vlm_img_demos_folder", - "demo_with_vlm_imgs" + "demo_with_vlm_imgs", "geo_and_demo+labelled_atoms", + "geo_and_saved_vlm_img_demos_folder", + "geo_and_demo_with_vlm_imgs" ]: # In this case, the annotations are basically ground atoms! # We can use these to make GroundAtomTrajectories. From c005cebd1fddc687cf2c59547fe123deca4b7e0a Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Wed, 26 Jun 2024 17:04:38 -0400 Subject: [PATCH 18/38] Add text labels to objects in burger (#1678) * Initial commit. * Note true vlm atoms in utils.abstract() to make it easier to debug. * Run autoformatter. --- predicators/envs/burger.py | 103 ++++++++++++++++++++++++++++++++++++- predicators/settings.py | 1 + predicators/utils.py | 3 +- 3 files changed, 104 insertions(+), 3 deletions(-) diff --git a/predicators/envs/burger.py b/predicators/envs/burger.py index 61a70d9772..1ec3017688 100644 --- a/predicators/envs/burger.py +++ b/predicators/envs/burger.py @@ -405,6 +405,7 @@ def get_empty_cells(cls, state: State) -> Set[Tuple[int, int]]: return set(cells) def simulate(self, state: State, action: Action) -> State: + held_item = None # We assume only one of , , , # is not "null" in each action. # If each one was null, the action would be <0, 0, -1, 0, 0>. @@ -523,7 +524,10 @@ def render_state_plt( action: Optional[Action] = None, caption: Optional[str] = None) -> matplotlib.figure.Figure: figsize = (self.num_cols * 2, self.num_rows * 2) - fig, ax = plt.subplots(1, 1, figsize=figsize) + # The DPI has to be sufficiently high otherwise when the matplotlib + # figure gets converted to a PIL image, text in the image can become + # blurry. + fig, ax = plt.subplots(1, 1, figsize=figsize, dpi=200) plt.suptitle(caption, wrap=True) # Plot vertical lines @@ -536,7 +540,6 @@ def render_state_plt( # Draw robot x, y = self.get_position(self._robot, state) - # ax.plot(robot_col + 0.5, robot_row + 0.5, 'rs', markersize=20) robot_direction = self.enum_to_dir[state.get(self._robot, "dir")] robot_img = mpimg.imread( utils.get_env_asset_path(f"imgs/robot_{robot_direction}.png")) @@ -546,17 +549,50 @@ def render_state_plt( x + (1 - img_size[0]) / 2, x + (1 + img_size[0]) / 2, y + (1 - img_size[1]) / 2, y + (1 + img_size[1]) / 2 ]) + if CFG.burger_render_set_of_marks: + ax.text(x + 1 / 2, + y + (1 - img_size[1]) / 2, + self._robot.name, + fontsize=10, + color="red", + ha="center", + va="top", + bbox=dict(facecolor="black", + alpha=0.5, + boxstyle="square,pad=0.0")) # Draw grill x, y = self.get_position(self._grill, state) grill_img = mpimg.imread(utils.get_env_asset_path("imgs/grill.png")) ax.imshow(grill_img, extent=[x, x + 1, y, y + 1]) + if CFG.burger_render_set_of_marks: + ax.text(x + 1 / 2, + y + (1 - img_size[1]) / 2, + self._grill.name, + fontsize=10, + color="red", + ha="center", + va="top", + bbox=dict(facecolor="black", + alpha=0.5, + boxstyle="square,pad=0.0")) # Draw cutting board x, y = self.get_position(self._cutting_board, state) cutting_board_img = mpimg.imread( utils.get_env_asset_path("imgs/cutting_board.png")) ax.imshow(cutting_board_img, extent=[x, x + 1, y, y + 1]) + if CFG.burger_render_set_of_marks: + ax.text(x + 1 / 2, + y + (1 - img_size[1]) / 2, + self._cutting_board.name, + fontsize=10, + color="red", + ha="center", + va="top", + bbox=dict(facecolor="black", + alpha=0.5, + boxstyle="square,pad=0.0")) # Draw items type_to_img = { @@ -615,6 +651,69 @@ def render_state_plt( y + (1 + img_size[1]) / 2 ] ax.imshow(img, extent=extent, zorder=zorder) + if CFG.burger_render_set_of_marks: + if is_held: + ax.text(x + (1 + held_img_size[0]) * (1 / 2), + y + offset + held_img_size[1] / 2, + item.name, + fontsize=10, + color="red", + ha="left", + va="top", + bbox=dict(facecolor="black", + alpha=0.5, + boxstyle="square,pad=0.0")) + else: + if zorder > 0: + if self._On_holds( + state, [item, self._grill]) or self._On_holds( + state, [item, self._cutting_board]): + ax.text(x + 1 / 2, + y + (1 + img_size[1]) / 2, + item.name, + fontsize=10, + color="red", + ha="center", + va="bottom", + bbox=dict(facecolor="black", + alpha=0.5, + boxstyle="square,pad=0.0")) + else: + ax.text(x, + y + (0.1 * zorder) + (1 - img_size[1]) / 2, + item.name, + fontsize=10, + color="red", + ha="left", + va="top", + bbox=dict(facecolor="black", + alpha=0.5, + boxstyle="square,pad=0.0")) + else: + if not self._Clear_holds( + state, [item]) or not self._OnNothing_holds( + state, [item]): + ax.text(x, + y + (1 - img_size[1]) / 2, + item.name, + fontsize=10, + color="red", + ha="left", + va="top", + bbox=dict(facecolor="black", + alpha=0.5, + boxstyle="square,pad=0.0")) + else: + ax.text(x + 1 / 2, + y + (1 - img_size[1]) / 2, + item.name, + fontsize=10, + color="red", + ha="center", + va="top", + bbox=dict(facecolor="black", + alpha=0.5, + boxstyle="square,pad=0.0")) # Draw background floor_img = mpimg.imread( diff --git a/predicators/settings.py b/predicators/settings.py index 4a15f39a10..238d25d4f4 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -351,6 +351,7 @@ class GlobalSettings: grid_row_num_cells = 100 # burger env parameters + burger_render_set_of_marks = True gridworld_num_rows = 4 gridworld_num_cols = 4 diff --git a/predicators/utils.py b/predicators/utils.py index f503c8fc89..83bf8bba52 100644 --- a/predicators/utils.py +++ b/predicators/utils.py @@ -2308,7 +2308,8 @@ def abstract(state: State, for pred in vlm_preds: for choice in get_object_combinations(list(state), pred.types): vlm_atoms.add(GroundAtom(pred, choice)) - atoms |= query_vlm_for_atom_vals(vlm_atoms, state, vlm) + true_vlm_atoms = query_vlm_for_atom_vals(vlm_atoms, state, vlm) + atoms |= true_vlm_atoms return atoms From 079ed6d03312052ece7101c37c1e84797470af01 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Thu, 27 Jun 2024 08:08:21 -0700 Subject: [PATCH 19/38] Rename geometric predicates to prevent FD from freaking out (#1679) * clean implementation of renaming ft. ashay! * fix checks --- .../grammar_search_invention_approach.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/predicators/approaches/grammar_search_invention_approach.py b/predicators/approaches/grammar_search_invention_approach.py index 0b6d0b6439..5e6057f069 100644 --- a/predicators/approaches/grammar_search_invention_approach.py +++ b/predicators/approaches/grammar_search_invention_approach.py @@ -25,7 +25,8 @@ _PredicateSearchScoreFunction, create_score_function from predicators.settings import CFG from predicators.structs import Dataset, GroundAtom, GroundAtomTrajectory, \ - Object, ParameterizedOption, Predicate, Segment, State, Task, Type + Object, ParameterizedOption, Predicate, Segment, State, Task, Type, \ + VLMPredicate ################################################################################ # Programmatic classifiers # @@ -1032,6 +1033,17 @@ def learn_from_offline_dataset(self, dataset: Dataset) -> None: self._learned_predicates = self._select_predicates_by_clustering( candidates, self._initial_predicates, dataset, atom_dataset) logging.info("Done.") + # Now, rename these predicates to be compatible with PDDL planners! + renamed_predicates: Set[Predicate] = set() + for p in self._learned_predicates: + if isinstance(p, VLMPredicate): # pragma: no cover. + renamed_predicates.add(p) + continue + new_name = p.name.replace("(", "[").replace(")", + "]").replace(" ", "_") + renamed_pred = Predicate(new_name, p.types, p._classifier) # pylint:disable=protected-access + renamed_predicates.add(renamed_pred) + self._learned_predicates = renamed_predicates # Finally, learn NSRTs via superclass, using all the kept predicates. annotations = None if dataset.has_annotations: From a3b862bff48fc72ac8f0a2831b2424ce8a4a2355 Mon Sep 17 00:00:00 2001 From: Yichao Liang Date: Mon, 1 Jul 2024 19:09:45 +0100 Subject: [PATCH 20/38] Update pour option init to allow oracle to run when exclude all predicates (#1682) * Update pour option init * fix yapf --------- Co-authored-by: yichao-liang --- .gitignore | 2 ++ predicators/ground_truth_models/coffee/options.py | 8 +++++++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 9439aec1a5..6018f0798e 100644 --- a/.gitignore +++ b/.gitignore @@ -27,6 +27,8 @@ machines.txt tests/_fake_trajs tests/_fake_results sas_plan +downward/ +Gymnasium-Robotics/ # Jetbrains IDEs .idea/ diff --git a/predicators/ground_truth_models/coffee/options.py b/predicators/ground_truth_models/coffee/options.py index ff5bafba8d..55b2510310 100644 --- a/predicators/ground_truth_models/coffee/options.py +++ b/predicators/ground_truth_models/coffee/options.py @@ -131,6 +131,12 @@ def _TurnMachineOn_terminal(state: State, memory: Dict, ) # Pour + def _Pour_initiable(state: State, memory: Dict, + objects: Sequence[Object], params: Array) -> bool: + del memory, params + robot, jug, _ = objects + return Holding.holds(state, [robot, jug]) + def _Pour_terminal(state: State, memory: Dict, objects: Sequence[Object], params: Array) -> bool: del memory, params # unused @@ -142,7 +148,7 @@ def _Pour_terminal(state: State, memory: Dict, types=[robot_type, jug_type, cup_type], params_space=Box(0, 1, (0, )), policy=cls._create_pour_policy(), - initiable=lambda s, m, o, p: True, + initiable=_Pour_initiable, terminal=_Pour_terminal, ) From bbb90a2a307cb2d510288783b5b77ae339dd595f Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Mon, 1 Jul 2024 22:06:58 -0700 Subject: [PATCH 21/38] Fix weird Pillow issue (#1684) * see if we can fix mypy issue(s) * yapf * fix mypy * should be gtg --- predicators/datasets/generate_atom_trajs_with_vlm.py | 11 +++++++---- predicators/envs/kitchen.py | 4 +++- setup.py | 1 + 3 files changed, 11 insertions(+), 5 deletions(-) diff --git a/predicators/datasets/generate_atom_trajs_with_vlm.py b/predicators/datasets/generate_atom_trajs_with_vlm.py index 372c491c99..618c05f430 100644 --- a/predicators/datasets/generate_atom_trajs_with_vlm.py +++ b/predicators/datasets/generate_atom_trajs_with_vlm.py @@ -7,7 +7,7 @@ import re from functools import partial from pathlib import Path -from typing import Dict, Iterator, List, Optional, Sequence, Set, Tuple +from typing import Dict, Iterator, List, Optional, Sequence, Set, Tuple, cast import dill as pkl import numpy as np @@ -870,12 +870,15 @@ def create_ground_atom_data_from_saved_img_trajs( img_traj = [] state_traj: Optional[List[State]] = [] for state_num in range(num_states_in_traj): - curr_imgs = [] + curr_imgs: List[PIL.Image.Image] = [] curr_state_path = path.joinpath(str(state_num)) # NOTE: we assume all images are saved as jpg files. img_files = sorted(glob.glob(str(curr_state_path) + "/*.jpg")) - for img in img_files: - curr_imgs.append(PIL.Image.open(img)) + for img_file in img_files: + # PIL.Image.open returns an ImageFile, which is a subclass of + # an Image. + img = cast(PIL.Image.Image, PIL.Image.open(img_file)) + curr_imgs.append(img) img_traj.append(curr_imgs) state_file = curr_state_path / "state.p" if state_file.exists(): # pragma: no cover diff --git a/predicators/envs/kitchen.py b/predicators/envs/kitchen.py index bcf6547224..fadf4dde3a 100644 --- a/predicators/envs/kitchen.py +++ b/predicators/envs/kitchen.py @@ -415,7 +415,9 @@ def _reset_initial_state_from_seed(self, seed: int) -> Observation: # is anywhere between burners 2 and 4. Later, we might add # even more variation. kettle_coords = (-0.269, rng.uniform(0.4, 0.55), 1.626) - self._gym_env.set_body_position("kettle", kettle_coords) + self._gym_env.set_body_position( + "kettle", # type: ignore + kettle_coords) return { "state_info": self.get_object_centric_state_info(), "obs_images": self.render() diff --git a/setup.py b/setup.py index 3201638957..fd83c01e8c 100644 --- a/setup.py +++ b/setup.py @@ -20,6 +20,7 @@ "dill==0.3.5.1", "pyperplan", "pathos", + "pillow==10.3.0", "requests", "slack_bolt", "pybullet>=3.2.0", From 80c74e4652c582790b428592a35e7b46962f29db Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Mon, 1 Jul 2024 23:35:43 -0700 Subject: [PATCH 22/38] Kitchen goal complexity (#1685) * making progress * planning for new kitchen goal(s) works! * fix mypy * yeet --- predicators/envs/kitchen.py | 80 +++++++++++++++------ predicators/perception/kitchen_perceiver.py | 23 ++++-- 2 files changed, 75 insertions(+), 28 deletions(-) diff --git a/predicators/envs/kitchen.py b/predicators/envs/kitchen.py index fadf4dde3a..6201c2e2f8 100644 --- a/predicators/envs/kitchen.py +++ b/predicators/envs/kitchen.py @@ -90,10 +90,12 @@ class KitchenEnv(BaseEnv): at_pre_pushontop_x_atol = 1.0 # other tolerance for AtPrePushOnTop obj_name_to_pre_push_dpos = { - ("kettle", "on"): (-0.05, -0.25, 0.00), + ("kettle", "on"): (-0.05, -0.2, 0.00), ("kettle", "off"): (0.0, 0.0, 0.08), ("knob4", "on"): (-0.1, -0.12, 0.05), ("knob4", "off"): (0.05, -0.12, -0.05), + ("knob3", "on"): (0.0, -0.12, 0.05), + ("knob3", "off"): (0.12, -0.12, -0.05), ("light", "on"): (0.1, -0.05, -0.05), ("light", "off"): (-0.1, -0.05, -0.05), ("microhandle", "on"): (0.0, -0.1, 0.13), @@ -295,7 +297,8 @@ def reset(self, train_or_test: str, task_idx: int) -> Observation: # We now need to reset the underlying gym environment to the correct # state. seed = utils.get_task_seed(train_or_test, task_idx) - self._current_observation = self._reset_initial_state_from_seed(seed) + self._current_observation = self._reset_initial_state_from_seed( + seed, train_or_test) return self._copy_observation(self._current_observation) def simulate(self, state: State, action: Action) -> State: @@ -355,25 +358,39 @@ def goal_reached(self) -> bool: self._current_observation["state_info"]) kettle = self.object_name_to_object("kettle") burner4 = self.object_name_to_object("burner4") + burner3 = self.object_name_to_object("burner3") knob4 = self.object_name_to_object("knob4") + knob3 = self.object_name_to_object("knob3") light = self.object_name_to_object("light") goal_desc = self._current_task.goal_description - kettle_on_burner = self._OnTop_holds(state, [kettle, burner4]) + kettle_on_burner4 = self._OnTop_holds(state, [kettle, burner4]) + kettle_on_burner3 = self._OnTop_holds(state, [kettle, burner3]) knob4_turned_on = self.On_holds(state, [knob4]) + knob3_turned_on = self.On_holds(state, [knob3]) light_turned_on = self.On_holds(state, [light]) - kettle_boiling = self._KettleBoiling_holds(state, - [kettle, burner4, knob4]) + kettle_boiling4 = self._KettleBoiling_holds(state, + [kettle, burner4, knob4]) + kettle_boiling3 = self._KettleBoiling_holds(state, + [kettle, burner3, knob3]) if goal_desc == ("Move the kettle to the back burner and turn it on; " "also turn on the light"): - return kettle_on_burner and knob4_turned_on and light_turned_on - if goal_desc == "Move the kettle to the back burner": - return kettle_on_burner - if goal_desc == "Turn on the back burner": + return kettle_on_burner4 and knob4_turned_on and light_turned_on + if goal_desc == "Move the kettle to the back left burner": + return kettle_on_burner4 + if goal_desc == "Move the kettle to the back right burner": + return kettle_on_burner3 + if goal_desc == "Turn on the back left burner": return knob4_turned_on + if goal_desc == "Turn on the back right burner": + return knob3_turned_on if goal_desc == "Turn on the light": return light_turned_on - if goal_desc == "Move the kettle to the back burner and turn it on": - return kettle_boiling + if goal_desc == ("Move the kettle to the back left burner " + "and turn it on"): + return kettle_boiling4 + if goal_desc == ("Move the kettle to the back right burner " + "and turn it on"): + return kettle_boiling3 raise NotImplementedError(f"Unrecognized goal: {goal_desc}") def _get_tasks(self, num: int, @@ -385,39 +402,56 @@ def _get_tasks(self, num: int, ] goal_descriptions: List[str] = [] if CFG.kitchen_goals in ["all", "kettle_only"]: - goal_descriptions.append("Move the kettle to the back burner") + if train_or_test == "train": + goal_descriptions.append( + "Move the kettle to the back left burner") + else: + goal_descriptions.append( + "Move the kettle to the back right burner") if CFG.kitchen_goals in ["all", "knob_only"]: - goal_descriptions.append("Turn on the back burner") + if train_or_test == "train": + goal_descriptions.append("Turn on the back left burner") + else: + goal_descriptions.append("Turn on the back right burner") if CFG.kitchen_goals in ["all", "light_only"]: goal_descriptions.append("Turn on the light") if CFG.kitchen_goals in ["all", "boil_kettle"]: - goal_descriptions.append( - "Move the kettle to the back burner and turn it on") + if train_or_test == "train": + goal_descriptions.append( + "Move the kettle to the back left burner and turn it on") + else: + goal_descriptions.append( + "Move the kettle to the back right burner and turn it on") if CFG.kitchen_goals == "all": - desc = ("Move the kettle to the back burner and turn it on; also " - "turn on the light") + desc = ( + "Move the kettle to the back left burner and turn it on; also " + "turn on the light") goal_descriptions.append(desc) for task_idx in range(num): seed = utils.get_task_seed(train_or_test, task_idx) - init_obs = self._reset_initial_state_from_seed(seed) + init_obs = self._reset_initial_state_from_seed(seed, train_or_test) goal_idx = task_idx % len(goal_descriptions) goal_description = goal_descriptions[goal_idx] task = EnvironmentTask(init_obs, goal_description) tasks.append(task) return tasks - def _reset_initial_state_from_seed(self, seed: int) -> Observation: + def _reset_initial_state_from_seed(self, seed: int, + train_or_test: str) -> Observation: self._gym_env.reset(seed=seed) + kettle_x_coord = -0.269 + if train_or_test == "test": + kettle_x_coord = 0.169 + kettle_y_coord = 0.4 if CFG.kitchen_randomize_init_state: rng = np.random.default_rng(seed) # For now, we only randomize the state such that the kettle # is anywhere between burners 2 and 4. Later, we might add # even more variation. - kettle_coords = (-0.269, rng.uniform(0.4, 0.55), 1.626) - self._gym_env.set_body_position( - "kettle", # type: ignore - kettle_coords) + kettle_y_coord = rng.uniform(0.4, 0.55) + self._gym_env.set_body_position( # type: ignore + "kettle", (kettle_x_coord, kettle_y_coord, 1.626)) return { "state_info": self.get_object_centric_state_info(), "obs_images": self.render() diff --git a/predicators/perception/kitchen_perceiver.py b/predicators/perception/kitchen_perceiver.py index 577e4bd98d..33958d8ef7 100644 --- a/predicators/perception/kitchen_perceiver.py +++ b/predicators/perception/kitchen_perceiver.py @@ -21,28 +21,41 @@ def reset(self, env_task: EnvironmentTask) -> Task: KettleBoiling = pred_name_to_pred["KettleBoiling"] kettle = KitchenEnv.object_name_to_object("kettle") knob4 = KitchenEnv.object_name_to_object("knob4") + knob3 = KitchenEnv.object_name_to_object("knob3") burner4 = KitchenEnv.object_name_to_object("burner4") + burner3 = KitchenEnv.object_name_to_object("burner3") light = KitchenEnv.object_name_to_object("light") goal_desc = env_task.goal_description - if goal_desc == ("Move the kettle to the back burner and turn it on; " - "also turn on the light"): + if goal_desc == ( + "Move the kettle to the back left burner and turn it on; " + "also turn on the light"): goal = { GroundAtom(TurnedOn, [knob4]), GroundAtom(OnTop, [kettle, burner4]), GroundAtom(TurnedOn, [light]), } - elif goal_desc == "Move the kettle to the back burner": + elif goal_desc == "Move the kettle to the back left burner": goal = {GroundAtom(OnTop, [kettle, burner4])} - elif goal_desc == "Turn on the back burner": + elif goal_desc == "Move the kettle to the back right burner": + goal = {GroundAtom(OnTop, [kettle, burner3])} + elif goal_desc == "Turn on the back left burner": goal = { GroundAtom(TurnedOn, [knob4]), } + elif goal_desc == "Turn on the back right burner": + goal = { + GroundAtom(TurnedOn, [knob3]), + } elif goal_desc == "Turn on the light": goal = { GroundAtom(TurnedOn, [light]), } - elif goal_desc == "Move the kettle to the back burner and turn it on": + elif goal_desc == ("Move the kettle to the back left burner " + "and turn it on"): goal = {GroundAtom(KettleBoiling, [kettle, burner4, knob4])} + elif goal_desc == ("Move the kettle to the back right burner " + "and turn it on"): + goal = {GroundAtom(KettleBoiling, [kettle, burner3, knob3])} else: raise NotImplementedError(f"Unrecognized goal: {goal_desc}") return Task(state, goal) From d51907b89ba058fed4d9c9a4c8f4302434c729bb Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Tue, 2 Jul 2024 10:40:34 -0400 Subject: [PATCH 23/38] Add task variation to BurgerEnv (#1669) * Initial commit. * Undo debugging thing. * Adjust goalhack predicate and goal in tasks. * Debugging changes. * Run autoformatter and get some of the checks to pass. * Update BurgerEnv test after the grid was changed from 4x4 to 5x5 and task variation was added. * Run autoformatter. * Update tests to get better coverage. * Add comments and make small edits to pass checks. * Get more checks to pass. * Remove pdb statement. * Try to fix typing issue. * Fix incorrect usage of cast. --------- Co-authored-by: Nishanth Kumar --- .../img_option_diffs_label_history.txt | 2 +- predicators/envs/burger.py | 206 +++++++++++++----- predicators/settings.py | 4 +- tests/envs/test_burger.py | 137 ++++++++---- 4 files changed, 240 insertions(+), 109 deletions(-) diff --git a/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt index aaef54b9b7..5bf3793734 100644 --- a/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt +++ b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history.txt @@ -1,3 +1,3 @@ -You are a vision system for a robot. You are provided with two images corresponding to the states before and after a particular skill is executed. You are given a list of predicates below, and you are given the values of these predicates in the image before the skill is executed. Your job is to output the values of the following predicates in the image after the skill is executed. Pay careful attention to the changes between the two images to figure out which predicates change and which predicates do not change. For the predicates that change, list these separately at the end of your response. Note that in some scenes, there might be no changes. First, output a description of what changes happen between the before and after images. Next, output each predicate value in the after image as a bulleted list with each predicate and value on a different line. For each predicate value, provide an explanation as to why you labelled this predicate as having this particular value. Use the format: : . . +You are a vision system for a robot. You are provided with two images corresponding to the states before and after a particular skill is executed. You are given a list of predicates below, and you are given the values of these predicates in the image before the skill is executed. Your job is to output the values of the following predicates in the image after the skill is executed. Pay careful attention to the visual changes between the two images to figure out which predicates change and which predicates do not change. For the predicates that change, list these separately at the end of your response. Note that in some scenes, there might be no changes. First, output a description of what changes you expect to happen based on the skill that was just run, explicitly noting the skill that was run. Second, output a description of what visual changes you see happen between the before and after images, looking specifically at the objects involved in the skill's arguments, noting what objects these are. Next, output each predicate value in the after image as a bulleted list with each predicate and value on a different line. For each predicate value, provide an explanation as to why you labelled this predicate as having this particular value. Use the format: : . . Predicates: \ No newline at end of file diff --git a/predicators/envs/burger.py b/predicators/envs/burger.py index 1ec3017688..36fd55342d 100644 --- a/predicators/envs/burger.py +++ b/predicators/envs/burger.py @@ -132,71 +132,157 @@ def types(self) -> Set[Type]: self._grill_type, self._cutting_board_type } + def get_edge_cells_for_object_placement( + self, rng: np.random.Generator) -> List[Tuple[int, int]]: + """Selects edge cells such that if objects were placed in these cells, + the robot would never find itself adjacent to more than one object. + + This helper function assumes that the grid is 5x5. + + Public for use by tests. + """ + n_row = self.num_rows + n_col = self.num_cols + top = [(n_row - 1, col) for col in range(n_col)] + left = [(row, 0) for row in range(n_row)] + bottom = [(0, col) for col in range(n_col)] + right = [(row, n_col - 1) for row in range(n_row)] + corners = [(0, 0), (0, self.num_cols - 1), (self.num_rows - 1, 0), + (self.num_rows - 1, self.num_cols - 1)] + + # Pick edge cells for objects to be placed in such that the robot will + # never be adjacent to two objects at the same time. + # 1. Pick one edge to keep all its cells. + # 2. Pick one edge to lose two cells. + # 3. The cells we keep in the remaining two edges are determined by the + # previous choices. + # If this strategy is confusing to you, spend a few minutes drawing it + # out on graph paper. + + # We don't consider placing objects in the corners because the robot + # cannot interact with an object that is diagonally positioned. + edges = [top, left, bottom, right] + for i, edge in enumerate(edges): + edges[i] = [c for c in edge if c not in corners] + top, left, bottom, right = edges + # Without loss of generality, have the top edge keep all its cells. To + # generate other possibilities, we will later rotate the entire grid + # with some probability. + # Note that we can always keep cells that are in the "middle" of the + # edge -- we only need to worry about cells at the ends of an edge. + # If one edge keeps all its cells (call this edge A), then for the two + # edges that are adjacent to A, we can't choose the cell in each of + # these that is closest to A -- otherwise the robot could be adjacent + # to objects at once. Since our grid has 4 edges, this implies that + # one edge will have to lose two cells, and the others will lose one + # cell. + loses_two = edges[rng.choice([1, 2, 3])] + if loses_two == left: + left = left[1:len(left) - 1] + bottom = bottom[:-1] + right = right[:-1] + elif loses_two == bottom: + left = left[:-1] + bottom = bottom[1:len(bottom) - 1] + right = right[:-1] + elif loses_two == right: + left = left[:-1] + bottom = bottom[1:] + right = right[1:len(right) - 1] + edges = [top, left, bottom, right] + # Now, rotate the grid with some probability to cover the total set of + # possibilities for object placements that satisfy our constraint. To + # see why this rotation covers all the possibilities, draw it out on + # graph paper. + cells = top + left + bottom + right + rotate = rng.choice([0, 1, 2, 3]) + # Rotate 0 degrees. + if rotate == 0: + ret = cells + elif rotate == 1: + # Rotate 90 degrees. + ret = [(col, n_row - 1 - row) for row, col in cells] + elif rotate == 2: + # Rotate 180 degrees. + ret = [(n_row - 1 - row, n_col - 1 - col) for row, col in cells] + else: + # Rotate 270 degrees. + ret = [(col, row) for row, col in cells] + + return ret + def _get_tasks(self, num: int, rng: np.random.Generator) -> List[EnvironmentTask]: - del rng # unused tasks = [] state_dict = {} hidden_state = {} - # Add robot, grill, and cutting board - state_dict[self._robot] = { - "row": 2, - "col": 2, - "z": 0, - "fingers": 0.0, - "dir": 3 - } - state_dict[self._grill] = {"row": 2, "col": 3, "z": 0} - state_dict[self._cutting_board] = {"row": 1, "col": 3, "z": 0} - - # Add patty - patty = Object("patty", self._patty_type) - state_dict[patty] = {"row": 0, "col": 0, "z": 0} - hidden_state[patty] = {"is_cooked": 0.0, "is_held": 0.0} - - # Add tomato - tomato = Object("tomato", self._tomato_type) - state_dict[tomato] = {"row": 0, "col": 1, "z": 0} - hidden_state[tomato] = {"is_sliced": 0.0, "is_held": 0.0} - - # Add cheese - cheese = Object("cheese", self._cheese_type) - state_dict[cheese] = {"row": 3, "col": 0, "z": 0} - hidden_state[cheese] = {"is_held": 0.0} - - # Add top bun - top_bun = Object("top_bun", self._top_bun_type) - state_dict[top_bun] = {"row": 3, "col": 1, "z": 0} - hidden_state[top_bun] = {"is_held": 0.0} - - # Add bottom bun - bottom_bun = Object("bottom_bun", self._bottom_bun_type) - state_dict[bottom_bun] = {"row": 0, "col": 2, "z": 0} - hidden_state[bottom_bun] = {"is_held": 0.0} - - goal = { - # GroundAtom(self._On, [patty, bottom_bun]), - # GroundAtom(self._On, [cheese, patty]), - # GroundAtom(self._On, [tomato, cheese]), - # GroundAtom(self._On, [top_bun, tomato]), - # GroundAtom(self._IsCooked, [patty]), - # GroundAtom(self._IsSliced, [tomato]), - # GroundAtom(self._GoalHack, [bottom_bun, patty, cheese, tomato, - # top_bun]) - GroundAtom(self._On, [patty, bottom_bun]), - GroundAtom(self._On, [cheese, patty]), - GroundAtom(self._IsCooked, [patty]), - } - - alt_goal = { - GroundAtom(self._On, [patty, bottom_bun]), - GroundAtom(self._On, [cheese, patty]), - GroundAtom(self._GoalHack, - [bottom_bun, patty, cheese, tomato, top_bun]) - } + spots_for_objects = self.get_edge_cells_for_object_placement(rng) for _ in range(num): + shuffled_spots = spots_for_objects.copy() + rng.shuffle(shuffled_spots) + + # Add robot, grill, and cutting board + r, c = shuffled_spots[0] + state_dict[self._robot] = { + "row": 2, # assumes 5x5 grid + "col": 2, # assumes 5x5 grid + "z": 0, + "fingers": 0.0, + "dir": 3 + } + r, c = shuffled_spots[1] + state_dict[self._grill] = {"row": r, "col": c, "z": 0} + r, c = shuffled_spots[2] + state_dict[self._cutting_board] = {"row": r, "col": c, "z": 0} + + # Add patty + r, c = shuffled_spots[3] + patty = Object("patty", self._patty_type) + state_dict[patty] = {"row": r, "col": c, "z": 0} + hidden_state[patty] = {"is_cooked": 0.0, "is_held": 0.0} + + # Add tomato + r, c = shuffled_spots[4] + tomato = Object("tomato", self._tomato_type) + state_dict[tomato] = {"row": r, "col": c, "z": 0} + hidden_state[tomato] = {"is_sliced": 0.0, "is_held": 0.0} + + # Add cheese + r, c = shuffled_spots[5] + cheese = Object("cheese", self._cheese_type) + state_dict[cheese] = {"row": r, "col": c, "z": 0} + hidden_state[cheese] = {"is_held": 0.0} + + # Add top bun + r, c = shuffled_spots[6] + top_bun = Object("top_bun", self._top_bun_type) + state_dict[top_bun] = {"row": r, "col": c, "z": 0} + hidden_state[top_bun] = {"is_held": 0.0} + + # Add bottom bun + r, c = shuffled_spots[7] + bottom_bun = Object("bottom_bun", self._bottom_bun_type) + state_dict[bottom_bun] = {"row": r, "col": c, "z": 0} + hidden_state[bottom_bun] = {"is_held": 0.0} + + goal = { + GroundAtom(self._On, [patty, bottom_bun]), + GroundAtom(self._On, [cheese, patty]), + # GroundAtom(self._On, [tomato, cheese]), + # GroundAtom(self._On, [top_bun, tomato]), + GroundAtom(self._IsCooked, [patty]), + GroundAtom(self._IsSliced, [tomato]), + } + + alt_goal = { + GroundAtom(self._On, [patty, bottom_bun]), + GroundAtom(self._On, [cheese, patty]), + GroundAtom(self._GoalHack, + [bottom_bun, patty, cheese, tomato, top_bun]) + } + state = utils.create_state_from_dict(state_dict) state.simulator_state = {} state.simulator_state["state"] = hidden_state @@ -276,7 +362,7 @@ def _Holding_holds(self, state: State, objects: Sequence[Object]) -> bool: robot, item = objects assert state.simulator_state is not None assert "state" in state.simulator_state - return not self._HandEmpty_holds(state,[robot]) and \ + return not self._HandEmpty_holds(state, [robot]) and \ state.simulator_state["state"][item]["is_held"] > 0.5 def _On_holds(self, state: State, objects: Sequence[Object]) -> bool: @@ -308,14 +394,14 @@ def _Clear_holds(self, state: State, objects: Sequence[Object]) -> bool: def _GoalHack_holds(self, state: State, objects: Sequence[Object]) -> bool: # bottom, patty, cheese, tomato, top = objects - bottom, patty, cheese, _, _ = objects + bottom, patty, cheese, tomato, _ = objects atoms = [ self._On_holds(state, [patty, bottom]), self._On_holds(state, [cheese, patty]), # self._On_holds(state, [tomato, cheese]), # self._On_holds(state, [top, tomato]), self._IsCooked_holds(state, [patty]), - # self._IsSliced_holds(state, [tomato]) + self._IsSliced_holds(state, [tomato]) ] return all(atoms) diff --git a/predicators/settings.py b/predicators/settings.py index 238d25d4f4..a6adbb763b 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -352,8 +352,8 @@ class GlobalSettings: # burger env parameters burger_render_set_of_marks = True - gridworld_num_rows = 4 - gridworld_num_cols = 4 + gridworld_num_rows = 5 + gridworld_num_cols = 5 # parameters for random options approach random_options_max_tries = 100 diff --git a/tests/envs/test_burger.py b/tests/envs/test_burger.py index 30f91aa949..97b438410d 100644 --- a/tests/envs/test_burger.py +++ b/tests/envs/test_burger.py @@ -20,10 +20,15 @@ def test_burger(): "option_model_terminate_on_repeat": False, "sesame_max_skeletons_optimized": 1000, "sesame_max_samples_per_step": 1, - "sesame_task_planner": "fdopt" + "sesame_task_planner": "fdopt", + "num_train_tasks": 1, + "num_test_tasks": 1, + "seed": 0 }) - env = BurgerEnv() + # This should really test this for however many train/test tasks we will use + # in our experiments (e.g 50 for each instead of 1), but because the image + # rendering is so slow, I set this to 1 here. for task in env.get_train_tasks(): for obj in task.init: assert len(obj.type.feature_names) == len(task.init[obj]) @@ -40,30 +45,35 @@ def test_burger(): nsrts = get_gt_nsrts(env.get_name(), env.predicates, options) assert len(nsrts) == 15 task = env.get_train_tasks()[0] + # The nsrts that are commented out are used when the "full" goal is used + # in the tasks. Because we are still getting predicate invention to work + # in BurgerEnv, we are not yet using the "full" goal. Because the full goal + # won't be in the environment's task's goals, we won't test a plan that + # achieves that goal in the tests for now. MoveWhenFacingOneStack = [ n for n in nsrts if n.name == "MoveWhenFacingOneStack" ][0] MoveWhenFacingTwoStack = [ n for n in nsrts if n.name == "MoveWhenFacingTwoStack" ][0] - MoveWhenFacingThreeStack = [ - n for n in nsrts if n.name == "MoveWhenFacingThreeStack" - ][0] - MoveWhenFacingFourStack = [ - n for n in nsrts if n.name == "MoveWhenFacingFourStack" - ][0] + # MoveWhenFacingThreeStack = [ + # n for n in nsrts if n.name == "MoveWhenFacingThreeStack" + # ][0] + # MoveWhenFacingFourStack = [ + # n for n in nsrts if n.name == "MoveWhenFacingFourStack" + # ][0] MoveFromNothingToOneStack = [ n for n in nsrts if n.name == "MoveFromNothingToOneStack" ][0] MoveFromNothingToTwoStack = [ n for n in nsrts if n.name == "MoveFromNothingToTwoStack" ][0] - MoveFromNothingToFourStack = [ - n for n in nsrts if n.name == "MoveFromNothingToFourStack" - ][0] - MoveFromOneStackToThreeStack = [ - n for n in nsrts if n.name == "MoveFromOneStackToThreeStack" - ][0] + # MoveFromNothingToFourStack = [ + # n for n in nsrts if n.name == "MoveFromNothingToFourStack" + # ][0] + # MoveFromOneStackToThreeStack = [ + # n for n in nsrts if n.name == "MoveFromOneStackToThreeStack" + # ][0] PickSingleAdjacent = [n for n in nsrts if n.name == "PickSingleAdjacent"][0] PickFromStack = [n for n in nsrts if n.name == "PickFromStack"][0] @@ -82,7 +92,12 @@ def test_burger(): bottom_bun = [obj for obj in task.init if obj.name == "bottom_bun"][0] plan = [ - MoveWhenFacingOneStack.ground([robot, patty, grill]), + MoveFromNothingToOneStack.ground([robot, tomato]), + PickSingleAdjacent.ground([robot, tomato]), + MoveFromNothingToOneStack.ground([robot, cutting_board]), + Place.ground([robot, tomato, cutting_board]), + Slice.ground([robot, tomato, cutting_board]), + MoveWhenFacingTwoStack.ground([robot, patty, tomato, cutting_board]), PickSingleAdjacent.ground([robot, patty]), MoveFromNothingToOneStack.ground([robot, grill]), Place.ground([robot, patty, grill]), @@ -93,25 +108,40 @@ def test_burger(): MoveWhenFacingTwoStack.ground([robot, cheese, patty, bottom_bun]), PickSingleAdjacent.ground([robot, cheese]), MoveFromNothingToTwoStack.ground([robot, patty, bottom_bun]), - Place.ground([robot, cheese, patty]), - MoveWhenFacingThreeStack.ground( - [robot, tomato, cheese, patty, bottom_bun]), - PickSingleAdjacent.ground([robot, tomato]), - MoveFromNothingToOneStack.ground([robot, cutting_board]), - Place.ground([robot, tomato, cutting_board]), - Slice.ground([robot, tomato, cutting_board]), - PickFromStack.ground([robot, tomato, cutting_board]), - MoveFromOneStackToThreeStack.ground( - [robot, cheese, patty, bottom_bun, cutting_board]), - Place.ground([robot, tomato, cheese]), - MoveWhenFacingFourStack.ground( - [robot, top_bun, tomato, cheese, patty, bottom_bun]), - PickSingleAdjacent.ground([robot, top_bun]), - MoveFromNothingToFourStack.ground( - [robot, tomato, cheese, patty, bottom_bun]), - Place.ground([robot, top_bun, tomato]) + Place.ground([robot, cheese, patty]) ] + # plan = [ + # MoveWhenFacingOneStack.ground([robot, patty, grill]), + # PickSingleAdjacent.ground([robot, patty]), + # MoveFromNothingToOneStack.ground([robot, grill]), + # Place.ground([robot, patty, grill]), + # Cook.ground([robot, patty, grill]), + # PickFromStack.ground([robot, patty, grill]), + # MoveWhenFacingOneStack.ground([robot, bottom_bun, grill]), + # Place.ground([robot, patty, bottom_bun]), + # MoveWhenFacingTwoStack.ground([robot, cheese, patty, bottom_bun]), + # PickSingleAdjacent.ground([robot, cheese]), + # MoveFromNothingToTwoStack.ground([robot, patty, bottom_bun]), + # Place.ground([robot, cheese, patty]), + # MoveWhenFacingThreeStack.ground( + # [robot, tomato, cheese, patty, bottom_bun]), + # PickSingleAdjacent.ground([robot, tomato]), + # MoveFromNothingToOneStack.ground([robot, cutting_board]), + # Place.ground([robot, tomato, cutting_board]), + # Slice.ground([robot, tomato, cutting_board]), + # PickFromStack.ground([robot, tomato, cutting_board]), + # MoveFromOneStackToThreeStack.ground( + # [robot, cheese, patty, bottom_bun, cutting_board]), + # Place.ground([robot, tomato, cheese]), + # MoveWhenFacingFourStack.ground( + # [robot, top_bun, tomato, cheese, patty, bottom_bun]), + # PickSingleAdjacent.ground([robot, top_bun]), + # MoveFromNothingToFourStack.ground( + # [robot, tomato, cheese, patty, bottom_bun]), + # Place.ground([robot, top_bun, tomato]) + # ] + option_plan = [n.option.ground(n.option_objs, []) for n in plan] policy = utils.option_plan_to_policy(option_plan) traj, _ = utils.run_policy(policy, @@ -125,11 +155,10 @@ def test_burger(): utils.HumanDemonstrationFailure, }, monitor=None) + assert task.task.goal_holds(traj.states[-1]) # Test _AdjacentToNothing_holds - state = task.init - state.set(robot, "col", 1) - state.set(top_bun, "col", 2) + state = task.init.copy() abstract_state = utils.abstract(state, env.predicates) AdjacentToNothing = [ p for p in env.predicates if p.name == "AdjacentToNothing" @@ -139,8 +168,8 @@ def test_burger(): # Test _OnNothing_holds OnNothing = [p for p in env.predicates if p.name == "OnNothing"][0] assert GroundAtom(OnNothing, - [top_bun]) not in utils.abstract(traj.states[-1], - env.predicates) + [cheese]) not in utils.abstract(traj.states[-1], + env.predicates) # Test _GoalHack_holds GoalHack = [p for p in env.predicates if p.name == "GoalHack"][0] @@ -151,25 +180,29 @@ def test_burger(): # Test get_cell_in_direction x, y = env.get_cell_in_direction(1, 1, "left") assert x == 0 and y == 1 + x, y = env.get_cell_in_direction(1, 1, "right") + assert x == 2 and y == 1 x, y = env.get_cell_in_direction(1, 1, "up") assert x == 1 and y == 2 + x, y = env.get_cell_in_direction(1, 1, "down") + assert x == 1 and y == 0 x, y = env.get_cell_in_direction(1, 1, "no_change") assert x == 1 and y == 1 # Test collision - state.set(robot, "col", 2) # robot is at (2, 2) + state.set(robot, "col", 3) # robot is at (2, 3)) action = Action(np.array([1, 0, -1, 0, 0], dtype=np.float32)) next_state = env.simulate(state, action) assert env.get_position(robot, next_state) == env.get_position(robot, state) # Test placing on the ground - state = traj.states[5] + state = traj.states[2] action = Action(np.array([0, 0, -1, 0, 1], dtype=np.float32)) next_state = env.simulate(state, action) - assert env.get_position(patty, next_state) == env.get_position( - patty, traj.states[4]) - assert next_state.get(patty, "z") == 0 + assert env.get_position(tomato, next_state) == env.get_position( + tomato, traj.states[0]) + assert next_state.get(tomato, "z") == 0 # Test rendering env.render_state_plt(traj.states[0], task) @@ -190,7 +223,7 @@ def test_burger(): plt.close() # Test move option when already adjacent but not facing - state = task.init + state = task.init.copy() state.set(grill, "col", 2) state.set(grill, "row", 3) Move = [o for o in options if o.name == "Move"][0] @@ -200,7 +233,7 @@ def test_burger(): next_state = env.step(action) assert next_state.get(robot, "dir") == 0 - state = task.init + state = task.init.copy() state.set(grill, "col", 2) state.set(grill, "row", 1) Move = [o for o in options if o.name == "Move"][0] @@ -210,7 +243,7 @@ def test_burger(): next_state = env.step(action) assert next_state.get(robot, "dir") == 2 - state = task.init + state = task.init.copy() state.set(grill, "col", 1) state.set(grill, "row", 2) Move = [o for o in options if o.name == "Move"][0] @@ -220,7 +253,7 @@ def test_burger(): next_state = env.step(action) assert next_state.get(robot, "dir") == 1 - state = task.init + state = task.init.copy() state.set(grill, "col", 3) state.set(grill, "row", 2) state.set(robot, "dir", 1) @@ -230,3 +263,15 @@ def test_burger(): action = option.policy(state) next_state = env.step(action) assert next_state.get(robot, "dir") == 3 + + # Test _get_edge_cells_for_object_placement() + # This isn't a real test because we aren't going to verify that the + # edge cells that this function outputs satisfy all the constraints we want + # them to satisy (edge cells such that the robot will never be adjacent to + # more than one of these edge cells at any time). + rng = np.random.default_rng(0) + # 50 is an arbitrary number here. We just want to call this function many + # times to get all possible outcomes to happen at least once for coverage + # purposes. + for _ in range(50): + _ = env.get_edge_cells_for_object_placement(rng) From ad168e60ba5a804f552b1508eb676b8d015a4d94 Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Tue, 2 Jul 2024 19:08:09 -0400 Subject: [PATCH 24/38] Parallelize vlm labeling (#1686) * Initial commit. * Add printing of traj number in labeling progress output. * Get checks to pass. * Add to test to get full coverage. * Refactor for cleaner code. * Fix typing issue. * Run autoformatter. * Add comment explaining use of multithreading. * Run autoformatter. --- .../datasets/generate_atom_trajs_with_vlm.py | 83 ++++++++++++++----- predicators/settings.py | 1 + tests/datasets/test_datasets.py | 33 ++++++++ 3 files changed, 95 insertions(+), 22 deletions(-) diff --git a/predicators/datasets/generate_atom_trajs_with_vlm.py b/predicators/datasets/generate_atom_trajs_with_vlm.py index 618c05f430..2affe19e0c 100644 --- a/predicators/datasets/generate_atom_trajs_with_vlm.py +++ b/predicators/datasets/generate_atom_trajs_with_vlm.py @@ -5,6 +5,7 @@ import logging import os import re +from concurrent.futures import ThreadPoolExecutor from functools import partial from pathlib import Path from typing import Dict, Iterator, List, Optional, Sequence, Set, Tuple, cast @@ -173,33 +174,71 @@ def _sample_vlm_atom_proposals_from_trajectories( return aggregated_vlm_output_strs +def _label_single_trajectory_with_vlm_atom_values(indexed_traj: Tuple[ + int, ImageOptionTrajectory], vlm: VisionLanguageModel, + atoms_list: List[str], + num_trajs: int) -> List[str]: + """Given a list of atoms, label every state in an ImageOptionTrajectory + with the truth values of those atoms.""" + idx, traj = indexed_traj + curr_scenes_labelled = 0 + total_scenes_to_label = len(traj.imgs) + curr_traj_txt_outputs: List[str] = [] + prompts_for_traj = _generate_prompt_for_scene_labelling( + traj, atoms_list, label_history=curr_traj_txt_outputs) + for text_prompt, img_prompt in prompts_for_traj: + # Sample VLM outputs with temperature 0 in an attempt to be + # accurate. + curr_vlm_atom_labelling = vlm.sample_completions(text_prompt, + img_prompt, + 0.0, + CFG.seed, + num_completions=1) + assert len(curr_vlm_atom_labelling) == 1 + sanitized_output = curr_vlm_atom_labelling[0].replace('\\', '') + curr_traj_txt_outputs.append(sanitized_output) + curr_scenes_labelled += 1 + logging.info( + f"Completed ({curr_scenes_labelled}/{total_scenes_to_label}) " \ + f"label queries to VLM for trajectory #{idx + 1} of {num_trajs}!" + ) + logging.info("Finished labeling trajectory.") + return curr_traj_txt_outputs + + def _label_trajectories_with_vlm_atom_values( trajectories: List[ImageOptionTrajectory], vlm: VisionLanguageModel, atoms_list: List[str]) -> List[List[str]]: """Given a list of atoms, label every state in ImageOptionTrajectories with - the truth values of a set of atoms.""" - total_scenes_to_label = sum(len(traj.imgs) for traj in trajectories) - curr_scenes_labelled = 0 + the truth values of those atoms.""" output_labelled_atoms_txt_list = [] - for traj in trajectories: - curr_traj_txt_outputs: List[str] = [] - prompts_for_traj = _generate_prompt_for_scene_labelling( - traj, atoms_list, label_history=curr_traj_txt_outputs) - for text_prompt, img_prompt in prompts_for_traj: - # Sample VLM outputs with temperature 0 in an attempt to be - # accurate. - curr_vlm_atom_labelling = vlm.sample_completions(text_prompt, - img_prompt, - 0.0, - CFG.seed, - num_completions=1) - assert len(curr_vlm_atom_labelling) == 1 - sanitized_output = curr_vlm_atom_labelling[0].replace('\\', '') - curr_traj_txt_outputs.append(sanitized_output) - curr_scenes_labelled += 1 - logging.info("Completed (%s/%s) label queries to VLM!", - curr_scenes_labelled, total_scenes_to_label) - output_labelled_atoms_txt_list.append(curr_traj_txt_outputs) + + def label_function( + indexed_traj: Tuple[int, ImageOptionTrajectory]) -> List[str]: + return _label_single_trajectory_with_vlm_atom_values( + indexed_traj, vlm, atoms_list, len(trajectories)) + + indexed_trajectories = list(enumerate(trajectories)) + + if CFG.grammar_search_parallelize_vlm_labeling: + logging.info("Labeling trajectories in parallel.") + # Mulithreading is the right choice over multiprocessing for tasks that + # involve waiting for responses from external servers, such as API + # calls, as these tasks are I/O bound. The python GIL has minimal impact + # on I/O-bound tasks. We will spawn one thread for each trajectory we + # need to label. The number of threads you can spawn is limited by + # system resources; the limit will far exceed the number of demo + # trajectories we expect, which is ~50. + with ThreadPoolExecutor() as executor: + for traj_txt_outputs in executor.map(label_function, + indexed_trajectories): + output_labelled_atoms_txt_list.append(traj_txt_outputs) + return output_labelled_atoms_txt_list + + logging.info("Labeling trajectories sequentially.") + for idx, traj in indexed_trajectories: + traj_txt_outputs = label_function((idx, traj)) + output_labelled_atoms_txt_list.append(traj_txt_outputs) return output_labelled_atoms_txt_list diff --git a/predicators/settings.py b/predicators/settings.py index a6adbb763b..aecd64f4d1 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -674,6 +674,7 @@ class GlobalSettings: grammar_search_vlm_atom_proposal_prompt_type = "options_labels_whole_traj" grammar_search_vlm_atom_label_prompt_type = "per_scene_naive" grammar_search_vlm_atom_proposal_use_debug = False + grammar_search_parallelize_vlm_labeling = True # grammar search clustering algorithm parameters grammar_search_clustering_gmm_num_components = 10 diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py index 371d2e94e7..897a3f39a7 100644 --- a/tests/datasets/test_datasets.py +++ b/tests/datasets/test_datasets.py @@ -584,6 +584,39 @@ def test_loading_saved_vlm_img_demos_folder_non_dummy_goal(): # Remove regular files, ignore directories for filename in filenames: os.unlink(os.path.join(dirpath, filename)) + # Coverage for code path where VLM trajectory labeling is not parallelized + utils.reset_config({ + "env": "cover", + "num_train_tasks": 1, + "offline_data_method": "saved_vlm_img_demos_folder", + "data_dir": "tests/datasets/mock_vlm_datasets", + "seed": 456, + "vlm_trajs_folder_name": "cover__vlm_demos__456__1", + "grammar_search_vlm_atom_proposal_prompt_type": "naive_each_step", + "grammar_search_vlm_atom_label_prompt_type": "per_scene_naive", + "pretrained_model_prompt_cache_dir": + "tests/datasets/mock_vlm_datasets/cache", + "cover_num_blocks": 1, + "cover_num_targets": 1, + "cover_block_widths": [0.1], + "cover_target_widths": [0.05], + "excluded_predicates": "all", + "grammar_search_parallelize_vlm_labeling": False + }) + env = CoverEnv() + train_tasks = env.get_train_tasks() + predicates, _ = utils.parse_config_excluded_predicates(env) + vlm = _DummyVLM() + loaded_dataset = create_ground_atom_data_from_saved_img_trajs( + env, train_tasks, predicates, get_gt_options(env.get_name()), vlm) + assert len(loaded_dataset.trajectories) == 1 + assert len(loaded_dataset.annotations) == 1 + assert "DummyGoal" not in str(loaded_dataset.annotations[0][-1]) + for dirpath, _, filenames in os.walk( + CFG.pretrained_model_prompt_cache_dir): + # Remove regular files, ignore directories + for filename in filenames: + os.unlink(os.path.join(dirpath, filename)) @pytest.mark.parametrize( From 8272c02f5f7f93fa658d66fca640f154ce2a55d8 Mon Sep 17 00:00:00 2001 From: Alicia Li <102700848+matrixbalto@users.noreply.github.com> Date: Wed, 3 Jul 2024 13:51:19 -0400 Subject: [PATCH 25/38] Continuous Door Opening Actions and Fixed Light in GridRowDoor (#1680) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * clean * cleaner * rename open * formatting * cleaning * deleted print --------- Co-authored-by: “matrixbalto” <“ilfangliu@gmail.com”> Co-authored-by: Nishanth Kumar --- .../approaches/bridge_policy_approach.py | 2 +- predicators/envs/grid_row.py | 54 ++++++++++----- .../ground_truth_models/grid_row/nsrts.py | 54 +++++++++++---- .../ground_truth_models/grid_row/options.py | 41 +++++++---- tests/envs/test_grid_row.py | 69 +++++++++---------- 5 files changed, 140 insertions(+), 80 deletions(-) diff --git a/predicators/approaches/bridge_policy_approach.py b/predicators/approaches/bridge_policy_approach.py index 4a49f7a2b7..d8a1987c74 100644 --- a/predicators/approaches/bridge_policy_approach.py +++ b/predicators/approaches/bridge_policy_approach.py @@ -414,7 +414,7 @@ def call_planner_policy(self, state: State, _: Dict, __: Sequence[Object], raise_error_on_repeated_state=True, ) - return Action(np.array([0.0, 0.0, 0.0], dtype=np.float32)) + return Action(np.array([0.0, 0.0, 0.0, 0.0], dtype=np.float32)) def call_planner_nsrt(self) -> NSRT: """CallPlanner NSRT.""" diff --git a/predicators/envs/grid_row.py b/predicators/envs/grid_row.py index c2090b2ce7..94b7a86b17 100644 --- a/predicators/envs/grid_row.py +++ b/predicators/envs/grid_row.py @@ -188,8 +188,9 @@ class GridRowDoorEnv(GridRowEnv): def __init__(self, use_gui: bool = True) -> None: super().__init__(use_gui) - # type door with features ['x', and 'open'] - self._door_type = Type("door", ["x", 'open']) + self._door_type = Type( + "door", + ["x", "move_key", "move_target", "turn_key", "turn_target"]) self._door = Object("door", self._door_type) self._DoorInCell = Predicate("DoorInCell", @@ -241,7 +242,13 @@ def render_state_plt( facecolor="white") ax.add_patch(light) door_pos = state.get(self._door, "x") - if state.get(self._door, "open"): + door_move_key = state.get(self._door, "move_key") + door_move_key_target = state.get(self._door, "move_target") + door_turn_key = state.get(self._door, "turn_key") + door_turn_key_target = state.get(self._door, "turn_target") + if (door_move_key_target - 0.1 <= door_move_key <= \ + door_move_key_target + 0.1 and door_turn_key_target - 0.1 \ + <= door_turn_key <= door_turn_key_target + 0.1): door = plt.Rectangle((door_pos - self.cell_width / 2.0, 0), self.cell_width, self.cell_width, @@ -279,8 +286,8 @@ def types(self) -> Set[Type]: @property def action_space(self) -> Box: - # dx, dlight, ddoor - return Box(-np.inf, np.inf, (3, )) + # dx, dlight, dmove, dturn + return Box(-np.inf, np.inf, (4, )) def _get_tasks(self, num: int, rng: np.random.Generator) -> List[EnvironmentTask]: @@ -301,7 +308,10 @@ def _get_tasks(self, num: int, }, self._door: { "x": len(self._cells) // 2 + 0.5, - "open": 0.0 + "move_key": 0.0, + "move_target": 0.5, + "turn_key": 0.0, + "turn_target": 0.75 } } for i, cell in enumerate(self._cells): @@ -313,10 +323,13 @@ def _get_tasks(self, num: int, def simulate(self, state: State, action: Action) -> State: assert self.action_space.contains(action.arr) next_state = state.copy() - dx, dlight, ddoor = action.arr + dx, dlight, dmove, dturn = action.arr door_pos = state.get(self._door, "x") robbot_pos = state.get(self._robot, "x") - door_open = state.get(self._door, "open") + door_move_key = state.get(self._door, "move_key") + door_move_target = state.get(self._door, "move_target") + door_turn_key = state.get(self._door, "turn_key") + door_turn_target = state.get(self._door, "turn_target") robot_cells = [ c for c in self._cells if self._In_holds(state, [self._robot, c]) ] @@ -325,10 +338,18 @@ def simulate(self, state: State, action: Action) -> State: ] assert len(door_cells) == 1 # Apply ddoor if we're in same cell as door + # Can only open door, not close door_cell = door_cells[0] robot_cell = robot_cells[0] - if robot_cell == door_cell and not door_open and ddoor == 1.0: - next_state.set(self._door, "open", 1.0) + if robot_cell == door_cell and not (door_move_target - 0.1 \ + <= door_move_key <= door_move_target + 0.1 \ + and door_turn_target - 0.1 <= door_turn_key <= door_turn_target + 0.1): + new_door_level = np.clip( + state.get(self._door, "move_key") + dmove, 0.0, 1.0) + next_state.set(self._door, "move_key", new_door_level) + new_door1_level = np.clip( + state.get(self._door, "turn_key") + dturn, 0.0, 1.0) + next_state.set(self._door, "turn_key", new_door1_level) # Apply dlight if we're in the same cell as the light. assert len(robot_cells) == 1 light_cells = [ @@ -336,16 +357,15 @@ def simulate(self, state: State, action: Action) -> State: ] assert len(light_cells) == 1 light_cell = light_cells[0] - if robot_cell == light_cell: - new_light_level = np.clip( - state.get(self._light, "level") + dlight, 0.0, 1.0) - next_state.set(self._light, "level", new_light_level) + if robot_cell == light_cell and dlight == 0.75 and self._LightOff_holds( + state, [self._light]): + next_state.set(self._light, "level", 0.75) - if door_open == 1.0 or (robbot_pos <= door_pos - and robbot_pos + dx <= door_pos): + if (door_move_target - 0.1 <= door_move_key <= door_move_target + 0.1 \ + and door_turn_target - 0.1 <= door_turn_key <= door_turn_target + 0.1) \ + or (robbot_pos <= door_pos and robbot_pos + dx <= door_pos): # Apply dx to robot. new_x = np.clip( state.get(self._robot, "x") + dx, 0.0, len(self._cells)) next_state.set(self._robot, "x", new_x) - return next_state diff --git a/predicators/ground_truth_models/grid_row/nsrts.py b/predicators/ground_truth_models/grid_row/nsrts.py index fe476b49ac..7e3c08a791 100644 --- a/predicators/ground_truth_models/grid_row/nsrts.py +++ b/predicators/ground_truth_models/grid_row/nsrts.py @@ -178,16 +178,18 @@ def get_nsrts(env_name: str, types: Dict[str, Type], MoveRobot = options["MoveRobot"] TurnOnLight = options["TurnOnLight"] TurnOffLight = options["TurnOffLight"] - OpenDoor = options["OpenDoor"] + # OpenDoor = options["OpenDoor"] + MoveKey = options["MoveKey"] + TurnKey = options["TurnKey"] nsrts = set() def light_sampler(state: State, goal: Set[GroundAtom], - rng: np.random.Generator, + _: np.random.Generator, objs: Sequence[Object]) -> Array: del state, goal, objs # unused # Note: just return a random value from -1 to 1 - return np.array([rng.uniform(-1.0, 1.0)], dtype=np.float32) + return np.array([0.75], dtype=np.float32) # MoveRobot robot = Variable("?robot", robot_type) @@ -260,20 +262,44 @@ def light_sampler(state: State, goal: Set[GroundAtom], option, option_vars, light_sampler) nsrts.add(turn_light_off_nsrt) - # OpenDoor - def door_sampler(state: State, goal: Set[GroundAtom], - rng: np.random.Generator, - objs: Sequence[Object]) -> Array: + # MoveKey + def move_key_sampler(state: State, goal: Set[GroundAtom], + rng: np.random.Generator, + objs: Sequence[Object]) -> Array: del state, goal, objs # unused - # Note: return 1.0 to show door is now open - return np.array([rng.uniform(1.0, 1.0)], dtype=np.float32) + return np.array([rng.uniform(-1.0, 1.0)], dtype=np.float32) + + robot = Variable("?robot", robot_type) + current_cell = Variable("?current_cell", cell_type) + door = Variable("?door", door_type) + parameters = [robot, current_cell, door] + option_vars = parameters + option = MoveKey + preconditions = { + LiftedAtom(DoorInCell, [door, current_cell]), + LiftedAtom(RobotInCell, [robot, current_cell]), + } + add_effects = set() + delete_effects = set() + ignore_effects = set() + move_key_nsrt = NSRT("MoveKey", parameters, preconditions, add_effects, + delete_effects, ignore_effects, option, + option_vars, move_key_sampler) + nsrts.add(move_key_nsrt) + + # TurnKey + def turn_key_sampler(state: State, goal: Set[GroundAtom], + rng: np.random.Generator, + objs: Sequence[Object]) -> Array: + del state, goal, objs # unused + return np.array([rng.uniform(-1.0, 1.0)], dtype=np.float32) robot = Variable("?robot", robot_type) current_cell = Variable("?current_cell", cell_type) door = Variable("?door", door_type) parameters = [robot, current_cell, door] option_vars = parameters - option = OpenDoor + option = TurnKey preconditions = { LiftedAtom(DoorInCell, [door, current_cell]), LiftedAtom(RobotInCell, [robot, current_cell]), @@ -281,9 +307,9 @@ def door_sampler(state: State, goal: Set[GroundAtom], add_effects = set() delete_effects = set() ignore_effects = set() - open_door_nsrt = NSRT("OpenDoor", parameters, preconditions, - add_effects, delete_effects, ignore_effects, - option, option_vars, door_sampler) - nsrts.add(open_door_nsrt) + turn_key_nsrt = NSRT("TurnKey", parameters, preconditions, add_effects, + delete_effects, ignore_effects, option, + option_vars, turn_key_sampler) + nsrts.add(turn_key_nsrt) return nsrts diff --git a/predicators/ground_truth_models/grid_row/options.py b/predicators/ground_truth_models/grid_row/options.py index ce513b5665..07d33357c1 100644 --- a/predicators/ground_truth_models/grid_row/options.py +++ b/predicators/ground_truth_models/grid_row/options.py @@ -107,7 +107,7 @@ def _MoveTo_policy(state: State, memory: Dict, rob_x = state.get(robot, "x") target_x = state.get(target_cell, "x") dx = target_x - rob_x - return Action(np.array([dx, 0.0, 0.0], dtype=np.float32)) + return Action(np.array([dx, 0.0, 0.0, 0.0], dtype=np.float32)) MoveRobot = utils.SingletonParameterizedOption( "MoveRobot", @@ -120,7 +120,7 @@ def _toggle_light_policy(state: State, memory: Dict, params: Array) -> Action: del state, objects, memory # unused dlight, = params - return Action(np.array([0.0, dlight, 0.0], dtype=np.float32)) + return Action(np.array([0.0, dlight, 0.0, 0.0], dtype=np.float32)) # TurnOnLight TurnOnLight = utils.SingletonParameterizedOption( @@ -138,19 +138,34 @@ def _toggle_light_policy(state: State, memory: Dict, params_space=Box(-1.0, 1.0, (1, )), ) - # OpenDoor - def _toggle_door_policy(state: State, memory: Dict, - objects: Sequence[Object], - params: Array) -> Action: + # MoveKey + def _move_key_policy(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> Action: del state, objects, memory # unused - ddoor, = params - return Action(np.array([0.0, 0.0, ddoor], dtype=np.float32)) + dmove, = params + return Action(np.array([0.0, 0.0, dmove, 0.0], dtype=np.float32)) - OpenDoor = utils.SingletonParameterizedOption( - "OpenDoor", + MoveKey = utils.SingletonParameterizedOption( + "MoveKey", types=[robot_type, cell_type, door_type], - policy=_toggle_door_policy, - params_space=Box(0.0, 1.0, (1, )), + policy=_move_key_policy, + params_space=Box(-1.0, 1.0, (1, )), + ) + + # TurnKey + def _turn_key_policy(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> Action: + del state, objects, memory # unused + dturn, = params + return Action(np.array([0.0, 0.0, 0.0, dturn], dtype=np.float32)) + + TurnKey = utils.SingletonParameterizedOption( + "TurnKey", + types=[robot_type, cell_type, door_type], + policy=_turn_key_policy, + params_space=Box(-1.0, 1.0, (1, )), ) - return {MoveRobot, TurnOnLight, TurnOffLight, OpenDoor} + return {MoveRobot, TurnOnLight, TurnOffLight, TurnKey, MoveKey} diff --git a/tests/envs/test_grid_row.py b/tests/envs/test_grid_row.py index 805d22ad4c..de4f379de9 100644 --- a/tests/envs/test_grid_row.py +++ b/tests/envs/test_grid_row.py @@ -173,9 +173,9 @@ def test_grid_row_door(): assert light_type.name == "light" assert robot_type.name == "robot" assert door_type.name == "door" - assert env.action_space.shape == (3, ) + assert env.action_space.shape == (4, ) nsrts = get_gt_nsrts(env.get_name(), env.predicates, options) - assert len(nsrts) == 4 + assert len(nsrts) == 5 assert len(options) == len(nsrts) env_train_tasks = env.get_train_tasks() assert len(env_train_tasks) == 1 @@ -184,8 +184,9 @@ def test_grid_row_door(): env_task = env_test_tasks[0] env.reset("test", 0) # Test NSRTs. - MoveRobot, OpenDoor, TurnOffLight, TurnOnLight = sorted(nsrts) - assert OpenDoor.name == "OpenDoor" + MoveKey, MoveRobot, TurnKey, TurnOffLight, TurnOnLight = sorted(nsrts) + assert MoveKey.name == "MoveKey" + assert TurnKey.name == "TurnKey" assert MoveRobot.name == "MoveRobot" assert TurnOffLight.name == "TurnOffLight" assert TurnOnLight.name == "TurnOnLight" @@ -203,13 +204,25 @@ def test_grid_row_door(): # First move to the light. state = init_state.copy() for cell, next_cell in zip(cell_order[:-1], cell_order[1:]): - if len(ground_nsrt_plan) == state.get(door, "x") - 0.5: - ground_nsrt_plan.append(OpenDoor.ground([robot, cell, door])) - ground_nsrt_plan.append(MoveRobot.ground([robot, cell, next_cell])) - else: - ground_nsrt_plan.append(MoveRobot.ground([robot, cell, next_cell])) - rng = np.random.default_rng(123) - for ground_nsrt in ground_nsrt_plan: + + if env._In_holds(state, [door, cell]): # pylint: disable=protected-access + for _ in range(100): + ground_nsrt = TurnKey.ground([robot, cell, door]) + option = ground_nsrt.sample_option(state, set(), rng) + action = option.policy(state) + state = env.simulate(state, action) + if 0.65 <= state.get(door, "turn_key") <= 0.85: + break + + ground_nsrt = MoveKey.ground([robot, cell, door]) + for _ in range(100): + option = ground_nsrt.sample_option(state, set(), rng) + action = option.policy(state) + state = env.simulate(state, action) + if 0.4 <= state.get(door, "move_key") <= 0.6: + break + ground_nsrt_plan.append(MoveRobot.ground([robot, cell, next_cell])) + ground_nsrt = ground_nsrt_plan[-1] assert all(a.holds(state) for a in ground_nsrt.preconditions) option = ground_nsrt.sample_option(state, set(), rng) assert option.initiable(state) @@ -219,31 +232,17 @@ def test_grid_row_door(): assert all(a.holds(state) for a in ground_nsrt.add_effects) assert not any(a.holds(state) for a in ground_nsrt.delete_effects) assert isinstance(env.render_state(state, task), list) - # Now repeatedly turn on the light until it succeeds. + + rng = np.random.default_rng(123) + + # Now turn on the light. ground_nsrt = TurnOnLight.ground([robot, cell_order[-1], light]) - for _ in range(100): - assert all(a.holds(state) for a in ground_nsrt.preconditions) - option = ground_nsrt.sample_option(state, set(), rng) - assert option.initiable(state) - action = option.policy(state) - state = env.simulate(state, action) - assert option.terminal(state) - if all(a.holds(state) for a in ground_nsrt.add_effects): - break - assert isinstance(env.render_state(state, task), list) - assert all(a.holds(state) for a in ground_nsrt.add_effects) - assert not any(a.holds(state) for a in ground_nsrt.delete_effects) - # Now repeatedly turn off the light until it succeeds. - ground_nsrt = TurnOffLight.ground([robot, cell_order[-1], light]) - for _ in range(100): - assert all(a.holds(state) for a in ground_nsrt.preconditions) - option = ground_nsrt.sample_option(state, set(), rng) - assert option.initiable(state) - action = option.policy(state) - state = env.simulate(state, action) - assert option.terminal(state) - if all(a.holds(state) for a in ground_nsrt.add_effects): - break + assert all(a.holds(state) for a in ground_nsrt.preconditions) + option = ground_nsrt.sample_option(state, set(), rng) + assert option.initiable(state) + action = option.policy(state) + state = env.simulate(state, action) + assert option.terminal(state) assert isinstance(env.render_state(state, task), list) assert all(a.holds(state) for a in ground_nsrt.add_effects) assert not any(a.holds(state) for a in ground_nsrt.delete_effects) From 1a167d664aee27a694e3c7945843108f5d4e3644 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Thu, 4 Jul 2024 23:32:34 -0700 Subject: [PATCH 26/38] No grammar search vlm invention baseline (#1688) --- .../grammar_search_invention_approach.py | 2 ++ .../test_grammar_search_invention_approach.py | 29 +++++++++++++++++++ 2 files changed, 31 insertions(+) diff --git a/predicators/approaches/grammar_search_invention_approach.py b/predicators/approaches/grammar_search_invention_approach.py index 5e6057f069..001ecc0d84 100644 --- a/predicators/approaches/grammar_search_invention_approach.py +++ b/predicators/approaches/grammar_search_invention_approach.py @@ -1032,6 +1032,8 @@ def learn_from_offline_dataset(self, dataset: Dataset) -> None: elif CFG.grammar_search_pred_selection_approach == "clustering": self._learned_predicates = self._select_predicates_by_clustering( candidates, self._initial_predicates, dataset, atom_dataset) + elif CFG.grammar_search_pred_selection_approach == "no_select": + self._learned_predicates = set(candidates.keys()) logging.info("Done.") # Now, rename these predicates to be compatible with PDDL planners! renamed_predicates: Set[Predicate] = set() diff --git a/tests/approaches/test_grammar_search_invention_approach.py b/tests/approaches/test_grammar_search_invention_approach.py index 140742b206..e33e4815b3 100644 --- a/tests/approaches/test_grammar_search_invention_approach.py +++ b/tests/approaches/test_grammar_search_invention_approach.py @@ -201,6 +201,35 @@ def test_invention_from_txt_file(): assert approach._get_current_predicates() == env.goal_predicates # pylint:disable=protected-access +def test_no_select_invention(): + """Test loading a dataset from a txt file using the no_select method.""" + utils.reset_config({ + "env": "ice_tea_making", + "num_train_tasks": 1, + "num_test_tasks": 0, + "offline_data_method": "demo+labelled_atoms", + "data_dir": "tests/datasets/mock_vlm_datasets", + "handmade_demo_filename": + "ice_tea_making__demo+labelled_atoms__manual__1.txt", + "grammar_search_pred_selection_approach": "no_select", + "disable_harmlessness_check": True + }) + env = IceTeaMakingEnv() + train_tasks = env.get_train_tasks() + predicates, _ = utils.parse_config_excluded_predicates(env) + loaded_dataset = create_dataset(env, train_tasks, + get_gt_options(env.get_name()), predicates) + approach = GrammarSearchInventionApproach(env.goal_predicates, + get_gt_options(env.get_name()), + env.types, env.action_space, + train_tasks) + approach.learn_from_offline_dataset(loaded_dataset) + # The ice_tea_making__demo+labelled_atoms__manual__1.txt happens to + # set all atoms to True at all timesteps. Normal predicate invention + # would only select the goal predicate, but in our case, we get more! + assert len(approach._get_current_predicates()) == 7 # pylint:disable=protected-access + + def test_geo_and_vlm_invention(): """Test constructing an atom dataset with both geo and vlm predicates.""" utils.reset_config({ From 61724fddf6a0a99470b3aaaa7f6aa5c44de5c19f Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Wed, 17 Jul 2024 11:37:13 +0200 Subject: [PATCH 27/38] Fixes + QoL improvements to the pretrained model interface (#1691) --- predicators/pretrained_model_interface.py | 57 +++++++++++++++++++---- predicators/utils.py | 13 +++++- tests/test_pretrained_model_interface.py | 16 ++++++- 3 files changed, 73 insertions(+), 13 deletions(-) diff --git a/predicators/pretrained_model_interface.py b/predicators/pretrained_model_interface.py index ee4ac685d2..dfd779b8f6 100644 --- a/predicators/pretrained_model_interface.py +++ b/predicators/pretrained_model_interface.py @@ -196,6 +196,22 @@ def call_openai_api(self, return completion.choices[0].message.content +class GoogleGeminiModel(): + """Common interface and methods for all Gemini-based models. + + Assumes that an environment variable GOOGLE_API_KEY is set with the + necessary API key to query the particular model name. + """ + + def __init__(self, model_name: str) -> None: + """See https://ai.google.dev/models/gemini for the list of available + model names.""" + self._model_name = model_name + assert "GOOGLE_API_KEY" in os.environ + genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) + self._model = genai.GenerativeModel(self._model_name) # pylint:disable=no-member + + class OpenAILLM(LargeLanguageModel, OpenAIModel): """Interface to openAI LLMs. @@ -225,7 +241,7 @@ def _sample_completions( stop_token: Optional[str] = None, num_completions: int = 1) -> List[str]: # pragma: no cover del imgs, seed, stop_token # unused - messages = [{"text": prompt, "type": "text"}] + messages = [{"role": "user", "content": prompt, "type": "text"}] responses = [ self.call_openai_api(messages, model=self._model_name, @@ -235,24 +251,44 @@ def _sample_completions( return responses -class GoogleGeminiVLM(VisionLanguageModel): +class GoogleGeminiLLM(LargeLanguageModel, GoogleGeminiModel): """Interface to the Google Gemini VLM (1.5). Assumes that an environment variable GOOGLE_API_KEY is set with the necessary API key to query the particular model name. """ - def __init__(self, model_name: str) -> None: - """See https://ai.google.dev/models/gemini for the list of available - model names.""" - self._model_name = model_name - assert "GOOGLE_API_KEY" in os.environ - genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) - self._model = genai.GenerativeModel(self._model_name) # pylint:disable=no-member + @retry(wait=wait_random_exponential(min=1, max=60), + stop=stop_after_attempt(10)) + def _sample_completions( + self, + prompt: str, + imgs: Optional[List[PIL.Image.Image]], + temperature: float, + seed: int, + stop_token: Optional[str] = None, + num_completions: int = 1) -> List[str]: # pragma: no cover + del seed, stop_token # unused + assert imgs is None + generation_config = genai.types.GenerationConfig( # pylint:disable=no-member + candidate_count=num_completions, + temperature=temperature) + response = self._model.generate_content( + [prompt], generation_config=generation_config) # type: ignore + response.resolve() + return [response.text] def get_id(self) -> str: return f"Google-{self._model_name}" + +class GoogleGeminiVLM(VisionLanguageModel, GoogleGeminiModel): + """Interface to the Google Gemini VLM (1.5). + + Assumes that an environment variable GOOGLE_API_KEY is set with the + necessary API key to query the particular model name. + """ + @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(10)) def _sample_completions( @@ -274,6 +310,9 @@ def _sample_completions( response.resolve() return [response.text] + def get_id(self) -> str: + return f"Google-{self._model_name}" + class OpenAIVLM(VisionLanguageModel, OpenAIModel): """Interface for OpenAI's VLMs, including GPT-4 Turbo (and preview diff --git a/predicators/utils.py b/predicators/utils.py index 83bf8bba52..f2034dbca1 100644 --- a/predicators/utils.py +++ b/predicators/utils.py @@ -44,8 +44,9 @@ from scipy.stats import beta as BetaRV from predicators.args import create_arg_parser -from predicators.pretrained_model_interface import GoogleGeminiVLM, \ - OpenAIVLM, VisionLanguageModel +from predicators.pretrained_model_interface import GoogleGeminiLLM, \ + GoogleGeminiVLM, LargeLanguageModel, OpenAILLM, OpenAIVLM, \ + VisionLanguageModel from predicators.pybullet_helpers.joint import JointPositions from predicators.settings import CFG, GlobalSettings from predicators.structs import NSRT, Action, Array, DummyOption, \ @@ -2234,6 +2235,14 @@ def create_vlm_by_name( return OpenAIVLM(model_name) +def create_llm_by_name( + model_name: str) -> LargeLanguageModel: # pragma: no cover + """Create particular llm using a provided name.""" + if "gemini" in model_name: + return GoogleGeminiLLM(model_name) + return OpenAILLM(model_name) + + def query_vlm_for_atom_vals( vlm_atoms: Collection[GroundAtom], state: State, diff --git a/tests/test_pretrained_model_interface.py b/tests/test_pretrained_model_interface.py index 51aa3d8d81..e1a3f10875 100644 --- a/tests/test_pretrained_model_interface.py +++ b/tests/test_pretrained_model_interface.py @@ -7,8 +7,9 @@ from PIL import Image from predicators import utils -from predicators.pretrained_model_interface import GoogleGeminiVLM, \ - LargeLanguageModel, OpenAILLM, OpenAIVLM, VisionLanguageModel +from predicators.pretrained_model_interface import GoogleGeminiLLM, \ + GoogleGeminiVLM, LargeLanguageModel, OpenAILLM, OpenAIVLM, \ + VisionLanguageModel class _DummyLLM(LargeLanguageModel): @@ -157,6 +158,17 @@ def test_gemini_vlm(): assert vlm.get_id() == "Google-gemini-pro-vision" +def test_gemini_llm(): + """Tests for GoogleGeminiLLM().""" + cache_dir = "_fake_llm_cache_dir" + utils.reset_config({"pretrained_model_prompt_cache_dir": cache_dir}) + if "GOOGLE_API_KEY" not in os.environ: # pragma: no cover + os.environ["GOOGLE_API_KEY"] = "dummy API key" + # Create an OpenAILLM with the curie model. + vlm = GoogleGeminiLLM("gemini-1.5-pro") + assert vlm.get_id() == "Google-gemini-1.5-pro" + + def test_openai_vlm(): """Tests for GoogleGeminiVLM().""" cache_dir = "_fake_llm_cache_dir" From 5397abb595a8c788ed9bf6f1cf6d3e5af2f038d5 Mon Sep 17 00:00:00 2001 From: Alicia Li <102700848+matrixbalto@users.noreply.github.com> Date: Thu, 25 Jul 2024 17:01:20 -0400 Subject: [PATCH 28/38] Added DoorKnobs Environment! (#1693) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * doorknobs added * testing * linting * more linting * refactor * more refactoring * small edit * cleaning * refactoring options * more refactoring * linter * minior fixes * coverage * more linting --------- Co-authored-by: “matrixbalto” <“ilfangliu@gmail.com”> Co-authored-by: NishanthJKumar --- predicators/envs/doors.py | 280 +++++++++++++++++- .../ground_truth_models/doors/nsrts.py | 156 ++++++---- .../ground_truth_models/doors/options.py | 225 +++++++++++++- predicators/settings.py | 4 + tests/envs/test_doors_env.py | 212 ++++++++++++- 5 files changed, 805 insertions(+), 72 deletions(-) diff --git a/predicators/envs/doors.py b/predicators/envs/doors.py index 4c6af5fc99..f95a5c4ce3 100644 --- a/predicators/envs/doors.py +++ b/predicators/envs/doors.py @@ -13,8 +13,8 @@ from predicators import utils from predicators.envs import BaseEnv from predicators.settings import CFG -from predicators.structs import Action, EnvironmentTask, GroundAtom, Object, \ - Predicate, State, Type +from predicators.structs import Action, Array, EnvironmentTask, GroundAtom, \ + Object, Predicate, State, Type from predicators.utils import Rectangle, StateWithCache, _Geom2D @@ -81,9 +81,10 @@ def __init__(self, use_gui: bool = True) -> None: def get_name(cls) -> str: return "doors" - def simulate(self, state: State, action: Action) -> State: + def simulate_moving(self, state: State, action: Action) -> State: + """helper function to simulate moving.""" assert self.action_space.contains(action.arr) - dx, dy, new_door_rot = action.arr + dx, dy, _ = action.arr x = state.get(self._robot, "x") y = state.get(self._robot, "y") new_x = x + dx @@ -96,6 +97,11 @@ def simulate(self, state: State, action: Action) -> State: # Revert the change to the robot position. next_state.set(self._robot, "x", x) next_state.set(self._robot, "y", y) + return next_state + + def simulate(self, state: State, action: Action) -> State: + new_door_rot = action.arr[2] + next_state = self.simulate_moving(state, action) # If touching a door, change its value based on the action. for door in state.get_objects(self._door_type): if self._TouchingDoor_holds(state, [self._robot, door]): @@ -292,9 +298,21 @@ def _sample_initial_state_from_map(self, room_map: NDArray, feat_dict = self._sample_door_feats( room_x, room_y, name, rng) state_dict[door] = feat_dict + + if self.get_name() == "doorknobs": + assert isinstance(self, DoorKnobsEnv) + #Create doorknobs + doorknob = Object( + f"room{task_id}-{r}-{c}-{name}-doorknob", + self._knob_type) + feat_dict = self._sample_doorknob_feats( + room_x, room_y, name, rng) + state_dict[doorknob] = feat_dict + self._door_to_knob[door] = doorknob # Sample obstacles for each room. Make them small and centered # enough that the robot should almost always be able to find a # collision-free path through the room. + for room in rooms: room_x = state_dict[room]["x"] room_y = state_dict[room]["y"] @@ -729,3 +747,257 @@ def _get_open_door_target_value(mass: float, friction: float, # A made up complicated function. return np.tanh(target_rot) * (np.sin(mass) + np.cos(friction) * np.sqrt(mass)) + + +class DoorKnobsEnv(DoorsEnv): + """A 2D navigation environment with obstacles, rooms, and doors.""" + # Types + _door_type = Type("door", ["x", "y", "theta", "mass", "friction", "open"]) + _knob_type = Type("knob", ["x", "y", "theta", "rot", "target_rot", "open"]) + open_door_thresh: ClassVar[float] = 0.1 + + def __init__(self, use_gui: bool = True) -> None: + super().__init__(use_gui) + self._door_to_knob: Dict[Object, Object] = {} + self._open_door_target_value = CFG.doorknobs_target_value + + @classmethod + def get_name(cls) -> str: + return "doorknobs" + + def simulate(self, state: State, action: Action) -> State: + new_door_rot = action.arr[2] + next_state = self.simulate_moving(state, action) + + # If touching a door, change its value based on the action. + for door in state.get_objects(self._door_type): + + if self._TouchingDoor_holds(state, [self._robot, door]): + # Get corresponding doorknob and rotate it accordingly + doorknob = self._door_to_knob[door] + new_door_level = np.clip( + state.get(doorknob, "rot") + new_door_rot, 0.0, 1.0) + next_state.set(doorknob, "rot", new_door_level) + # Check if we should now open the door. + target = self._open_door_target_value + if abs(new_door_level - target) < self.open_door_thresh: + next_state.set(door, "open", 1.0) + next_state.set(doorknob, "open", 1.0) + else: + next_state.set(door, "open", 0.0) + return next_state + + def _generate_train_tasks(self) -> List[EnvironmentTask]: + self._room_map_size = CFG.doors_room_map_size + return self._get_tasks(num=CFG.num_train_tasks, rng=self._train_rng) + + def _generate_test_tasks(self) -> List[EnvironmentTask]: + self._room_map_size = CFG.test_doors_room_map_size + return self._get_tasks(num=CFG.num_test_tasks, rng=self._test_rng) + + @property + def predicates(self) -> Set[Predicate]: + return { + self._InRoom, + self._InDoorway, + self._DoorInRoom, + self._InMainRoom, + self._DoorsShareRoom, + } + + @property + def goal_predicates(self) -> Set[Predicate]: + return {self._InRoom} + + @property + def types(self) -> Set[Type]: + return { + self._robot_type, self._door_type, self._knob_type, + self._room_type, self._obstacle_type + } + + @classmethod + def state_has_collision(cls, state: State, robot_pos: \ + Optional[Array] = None) -> bool: + """Public for use by oracle options.""" + robot, = state.get_objects(cls._robot_type) + robot_geom = cls.object_to_geom(robot, state, robot_pos) + # Check for collisions with obstacles. + for obstacle in state.get_objects(cls._obstacle_type): + obstacle_geom = cls.object_to_geom(obstacle, state) + if robot_geom.intersects(obstacle_geom): + return True + # Check for collisions with closed doors. + for door in state.get_objects(cls._door_type): + if cls._DoorIsOpen_holds(state, [door]): + continue + door_geom = cls.object_to_geom(door, state) + if robot_geom.intersects(door_geom): + return True + return False + + @property + def action_space(self) -> Box: + # dx, dy, drot + lb = np.array( + [-self.action_magnitude, -self.action_magnitude, -np.inf], + dtype=np.float32) + ub = np.array([self.action_magnitude, self.action_magnitude, np.inf], + dtype=np.float32) + return Box(lb, ub) + + def render_state_plt( + self, + state: State, + task: EnvironmentTask, + action: Optional[Action] = None, + caption: Optional[str] = None) -> matplotlib.figure.Figure: + del caption # unused + x_lb, x_ub, y_lb, y_ub = self._get_world_boundaries(state) + fig, ax = plt.subplots(1, 1, figsize=(x_ub - x_lb, y_ub - y_lb)) + + # Draw rooms. + default_room_color = "lightgray" + in_room_color = "lightsteelblue" + goal_room_color = "khaki" + goal_room = next(iter(task.goal)).objects[1] + for room in state.get_objects(self._room_type): + room_geom = self.object_to_geom(room, state) + if room == goal_room: + color = goal_room_color + elif self._InRoom_holds(state, [self._robot, room]): + color = in_room_color + else: + color = default_room_color + room_geom.plot(ax, color=color) + + # Draw robot. + robot_color = "blue" + robot_geom = self.object_to_geom(self._robot, state) + robot_geom.plot(ax, color=robot_color) + + # Draw obstacles (including room walls). + obstacle_color = "black" + for obstacle in state.get_objects(self._obstacle_type): + obstacle_geom = self.object_to_geom(obstacle, state) + obstacle_geom.plot(ax, color=obstacle_color) + + # Draw doors. + closed_door_color = "orangered" + open_door_color = "lightgreen" + doorway_color = "darkviolet" + for door in state.get_objects(self._door_type): + if self._DoorIsOpen_holds(state, [door]): + color = open_door_color + else: + color = closed_door_color + door_geom = self.object_to_geom(door, state) + door_geom.plot(ax, color=color) + if CFG.doors_draw_debug: + doorway_geom = self.door_to_doorway_geom(door, state) + doorway_geom.plot(ax, color=doorway_color, alpha=0.1) + + # Visualize the motion plan. + if CFG.doors_draw_debug: + if action is not None and action.has_option(): + option = action.get_option() + if "position_plan" in option.memory: + xs, ys = zip(*option.memory["position_plan"]) + plt.scatter(xs, ys, s=3, alpha=0.5, color=robot_color) + + pad = 2 * self.wall_depth + ax.set_xlim(x_lb - pad, x_ub + pad) + ax.set_ylim(y_lb - pad, y_ub + pad) + + plt.axis("off") + plt.tight_layout() + return fig + + @classmethod + def object_to_geom(cls, + obj: Object, + state: State, + robot_pos: Optional[Array] = None) -> _Geom2D: + """Public for use by oracle options.""" + if robot_pos is None: + x = state.get(obj, "x") + y = state.get(obj, "y") + else: + x, y = robot_pos + if obj.is_instance(cls._robot_type): + return utils.Circle(x, y, cls.robot_radius) + # Only the robot shape is dynamic. All other shapes are cached. + assert isinstance(state, StateWithCache) + static_geom_cache = state.cache["static_geom"] + if obj not in static_geom_cache: + if obj.is_instance(cls._room_type): + width = cls.room_size + height = cls.room_size + theta = 0.0 + elif obj.is_instance(cls._door_type): + width = cls.hallway_width + height = cls.wall_depth + theta = state.get(obj, "theta") + elif obj.is_instance(cls._knob_type): + width = cls.hallway_width # pragma: no cover + height = cls.wall_depth # pragma: no cover + theta = state.get(obj, "theta") # pragma: no cover + else: + assert obj.is_instance(cls._obstacle_type) + width = state.get(obj, "width") + height = state.get(obj, "height") + theta = state.get(obj, "theta") + geom = Rectangle(x=x, y=y, width=width, height=height, theta=theta) + static_geom_cache[obj] = geom + return static_geom_cache[obj] + + def _sample_door_feats(self, room_x: float, room_y: float, loc: str, + rng: np.random.Generator) -> Dict[str, float]: + # This is the length of the wall on one side of the door. + offset = (self.room_size + self.wall_depth - self.hallway_width) / 2 + + if loc == "bottom": + x = room_x + offset + y = room_y - self.wall_depth / 2 + theta = 0.0 + else: + assert loc == "left" + x = room_x + self.wall_depth / 2 + y = room_y + offset + theta = np.pi / 2 + + mass, friction, _ = rng.uniform(0.0, 1.0, size=(3, )) + return { + "x": x, + "y": y, + "theta": theta, + "mass": mass, + "friction": friction, + "open": 0.0, # always start out closed + } + + def _sample_doorknob_feats(self, room_x: float, room_y: float, loc: str, + _: np.random.Generator) -> Dict[str, float]: + # This is the length of the wall on one side of the door. + offset = (self.room_size + self.wall_depth - self.hallway_width) / 2 + + if loc == "bottom": + x = room_x + offset + y = room_y - self.wall_depth / 2 + theta = 0.0 + else: + assert loc == "left" + x = room_x + self.wall_depth / 2 + y = room_y + offset + theta = np.pi / 2 + + target_rot = 0.75 + # Sample the initial rotation so that the door is not yet opened. + return { + "x": x, + "y": y, + "theta": theta, + "rot": 0, + "target_rot": target_rot, + "open": 0.0, # always start out closed + } diff --git a/predicators/ground_truth_models/doors/nsrts.py b/predicators/ground_truth_models/doors/nsrts.py index af0698ebd7..8d26cce23f 100644 --- a/predicators/ground_truth_models/doors/nsrts.py +++ b/predicators/ground_truth_models/doors/nsrts.py @@ -5,7 +5,7 @@ import numpy as np from predicators.envs import get_or_create_env -from predicators.envs.doors import DoorsEnv +from predicators.envs.doors import DoorKnobsEnv, DoorsEnv from predicators.ground_truth_models import GroundTruthNSRTFactory from predicators.structs import NSRT, Array, GroundAtom, LiftedAtom, Object, \ ParameterizedOption, Predicate, State, Type, Variable @@ -32,11 +32,14 @@ def get_nsrts(env_name: str, types: Dict[str, Type], InRoom = predicates["InRoom"] InDoorway = predicates["InDoorway"] InMainRoom = predicates["InMainRoom"] - TouchingDoor = predicates["TouchingDoor"] - DoorIsOpen = predicates["DoorIsOpen"] + DoorInRoom = predicates["DoorInRoom"] DoorsShareRoom = predicates["DoorsShareRoom"] + if env_name == "doors": + TouchingDoor = predicates["TouchingDoor"] + DoorIsOpen = predicates["DoorIsOpen"] + # Options MoveToDoor = options["MoveToDoor"] OpenDoor = options["OpenDoor"] @@ -57,10 +60,11 @@ def get_nsrts(env_name: str, types: Dict[str, Type], LiftedAtom(InMainRoom, [robot, room]), LiftedAtom(DoorInRoom, [door, room]), } - add_effects = { - LiftedAtom(TouchingDoor, [robot, door]), - LiftedAtom(InDoorway, [robot, door]) - } + add_effects = {LiftedAtom(InDoorway, [robot, door])} + + if env_name == "doors": + add_effects.add(LiftedAtom(TouchingDoor, [robot, door])) + delete_effects = {LiftedAtom(InMainRoom, [robot, room])} ignore_effects: Set[Predicate] = set() move_to_door_nsrt = NSRT("MoveToDoorFromMainRoom", parameters, @@ -80,10 +84,10 @@ def get_nsrts(env_name: str, types: Dict[str, Type], LiftedAtom(InDoorway, [robot, start_door]), LiftedAtom(DoorsShareRoom, [start_door, end_door]), } - add_effects = { - LiftedAtom(TouchingDoor, [robot, end_door]), - LiftedAtom(InDoorway, [robot, end_door]) - } + add_effects = {LiftedAtom(InDoorway, [robot, end_door])} + if env_name == "doors": + add_effects.add(LiftedAtom(TouchingDoor, [robot, end_door])) + delete_effects = {LiftedAtom(InDoorway, [robot, start_door])} ignore_effects = set() move_to_door_nsrt = NSRT("MoveToDoorFromDoorWay", parameters, @@ -93,53 +97,79 @@ def get_nsrts(env_name: str, types: Dict[str, Type], nsrts.add(move_to_door_nsrt) # OpenDoor - robot = Variable("?robot", robot_type) - door = Variable("?door", door_type) - parameters = [door, robot] - option_vars = [door, robot] - option = OpenDoor - preconditions = { - LiftedAtom(TouchingDoor, [robot, door]), - LiftedAtom(InDoorway, [robot, door]), - } - add_effects = {LiftedAtom(DoorIsOpen, [door])} - delete_effects = { - LiftedAtom(TouchingDoor, [robot, door]), - } - ignore_effects = set() + if env_name == "doors": + robot = Variable("?robot", robot_type) + door = Variable("?door", door_type) + parameters = [door, robot] + option_vars = [door, robot] + option = OpenDoor + preconditions = { + LiftedAtom(TouchingDoor, [robot, door]), + LiftedAtom(InDoorway, [robot, door]), + } + add_effects = {LiftedAtom(DoorIsOpen, [door])} + delete_effects = { + LiftedAtom(TouchingDoor, [robot, door]), + } + ignore_effects = set() + + # Allow protected access because this is an oracle. \ + # Used in the sampler. + env = get_or_create_env(env_name) + assert isinstance(env, (DoorKnobsEnv, DoorsEnv)) + get_open_door_target_value = env._get_open_door_target_value # pylint: disable=protected-access + + # Even though this option does not need to be parameterized, + # we make it so, because we want to match the parameter + # space of the option that will get learned during option + # learning. This is useful for when we + # want to use sampler_learner = "oracle" too. + def open_door_sampler(state: State, goal: Set[GroundAtom], + rng: np.random.Generator, + objs: Sequence[Object]) -> Array: + del rng, goal # unused + door, _ = objs + assert door.is_instance(door_type) + # Calculate the desired change in the doors + # "rotation" feature. + # Allow protected access because this is an oracle. + mass = state.get(door, "mass") + friction = state.get(door, "friction") + target_rot = state.get(door, "target_rot") + target_val = get_open_door_target_value(mass=mass, + friction=friction, + target_rot=target_rot) + current_val = state.get(door, "rot") + delta_rot = target_val - current_val + # The door always changes from closed to open. + delta_open = 1.0 + return np.array([delta_rot, delta_open], dtype=np.float32) + + open_door_nsrt = NSRT("OpenDoor", parameters, preconditions, + add_effects, delete_effects, ignore_effects, + option, option_vars, open_door_sampler) + + elif env_name == "doorknobs": + robot = Variable("?robot", robot_type) + parameters = [robot] + option_vars = [robot] + option = OpenDoor + preconditions = set() + add_effects = set() + delete_effects = set() + ignore_effects = set() + + def open_doorknob_sampler(_: State, goal: Set[GroundAtom], + rng: np.random.Generator, + __: Sequence[Object]) -> Array: + del goal # unused # pragma: no cover + return np.array([rng.uniform(-1.0, 1.0)], + dtype=np.float32) # pragma: no cover + + open_door_nsrt = NSRT("OpenDoor", parameters, preconditions, + add_effects, delete_effects, ignore_effects, + option, option_vars, open_doorknob_sampler) - # Allow protected access because this is an oracle. Used in the sampler. - env = get_or_create_env(env_name) - assert isinstance(env, DoorsEnv) - get_open_door_target_value = env._get_open_door_target_value # pylint: disable=protected-access - - # Even though this option does not need to be parameterized, we make it - # so, because we want to match the parameter space of the option that - # will get learned during option learning. This is useful for when we - # want to use sampler_learner = "oracle" too. - def open_door_sampler(state: State, goal: Set[GroundAtom], - rng: np.random.Generator, - objs: Sequence[Object]) -> Array: - del rng, goal # unused - door, _ = objs - assert door.is_instance(door_type) - # Calculate the desired change in the doors "rotation" feature. - # Allow protected access because this is an oracle. - mass = state.get(door, "mass") - friction = state.get(door, "friction") - target_rot = state.get(door, "target_rot") - target_val = get_open_door_target_value(mass=mass, - friction=friction, - target_rot=target_rot) - current_val = state.get(door, "rot") - delta_rot = target_val - current_val - # The door always changes from closed to open. - delta_open = 1.0 - return np.array([delta_rot, delta_open], dtype=np.float32) - - open_door_nsrt = NSRT("OpenDoor", parameters, preconditions, - add_effects, delete_effects, ignore_effects, - option, option_vars, open_door_sampler) nsrts.add(open_door_nsrt) # MoveThroughDoor @@ -153,10 +183,12 @@ def open_door_sampler(state: State, goal: Set[GroundAtom], preconditions = { LiftedAtom(InRoom, [robot, start_room]), LiftedAtom(InDoorway, [robot, door]), - LiftedAtom(DoorIsOpen, [door]), LiftedAtom(DoorInRoom, [door, start_room]), LiftedAtom(DoorInRoom, [door, end_room]), } + if env_name == "doors": + preconditions.add(LiftedAtom(DoorIsOpen, [door])) + add_effects = { LiftedAtom(InRoom, [robot, end_room]), } @@ -171,3 +203,11 @@ def open_door_sampler(state: State, goal: Set[GroundAtom], nsrts.add(move_through_door_nsrt) return nsrts + + +class DoorknobsGroundTruthNSRTFactory(DoorsGroundTruthNSRTFactory): + """Ground-truth NSRTs for the doors environment.""" + + @classmethod + def get_env_names(cls) -> Set[str]: + return {"doorknobs"} diff --git a/predicators/ground_truth_models/doors/options.py b/predicators/ground_truth_models/doors/options.py index f81aa31f1e..d243e17df0 100644 --- a/predicators/ground_truth_models/doors/options.py +++ b/predicators/ground_truth_models/doors/options.py @@ -6,13 +6,14 @@ from gym.spaces import Box from predicators import utils -from predicators.envs.doors import DoorsEnv +from predicators.envs.doors import DoorKnobsEnv, DoorsEnv from predicators.ground_truth_models import GroundTruthOptionFactory from predicators.settings import CFG from predicators.structs import Action, Array, Object, \ ParameterizedInitiable, ParameterizedOption, ParameterizedPolicy, \ Predicate, State, Type -from predicators.utils import Rectangle, StateWithCache +from predicators.utils import Rectangle, SingletonParameterizedOption, \ + StateWithCache class DoorsGroundTruthOptionFactory(GroundTruthOptionFactory): @@ -22,6 +23,13 @@ class DoorsGroundTruthOptionFactory(GroundTruthOptionFactory): def get_env_names(cls) -> Set[str]: return {"doors"} + @staticmethod + def _MoveToDoor_policy(state: State, memory: Dict, + objects: Sequence[Object], params: Array) -> Action: + del state, objects, params # unused + assert memory["action_plan"], "Motion plan did not reach its goal" + return memory["action_plan"].pop(0) + @classmethod def get_options(cls, env_name: str, types: Dict[str, Type], predicates: Dict[str, Predicate], @@ -36,12 +44,6 @@ def get_options(cls, env_name: str, types: Dict[str, Type], InRoom = predicates["InRoom"] # MoveToDoor - def _MoveToDoor_policy(state: State, memory: Dict, - objects: Sequence[Object], - params: Array) -> Action: - del state, objects, params # unused - assert memory["action_plan"], "Motion plan did not reach its goal" - return memory["action_plan"].pop(0) def _MoveToDoor_terminal(state: State, memory: Dict, objects: Sequence[Object], @@ -57,7 +59,7 @@ def _MoveToDoor_terminal(state: State, memory: Dict, # No parameters; the option always moves to the doorway center. params_space=Box(0, 1, (0, )), # The policy is a motion planner. - policy=_MoveToDoor_policy, + policy=cls._MoveToDoor_policy, # Only initiable when the robot is in a room for the doory. initiable=cls._create_move_to_door_initiable(predicates), terminal=_MoveToDoor_terminal) @@ -293,3 +295,208 @@ def _get_position_in_doorway(cls, room: Object, door: Object, ty = (y0 + y1) / 2 position_cache[(room, door)] = (tx, ty) return position_cache[(room, door)] + + +class DoorknobsGroundTruthOptionFactory(DoorsGroundTruthOptionFactory): + """Ground-truth options for the doorknobs environment.""" + + @classmethod + def get_env_names(cls) -> Set[str]: + return {"doorknobs"} + + @classmethod + def get_options(cls, env_name: str, types: Dict[str, Type], + predicates: Dict[str, Predicate], + action_space: Box) -> Set[ParameterizedOption]: + + robot_type = types["robot"] + door_type = types["door"] + + InDoorway = predicates["InDoorway"] + InRoom = predicates["InRoom"] + + # MoveToDoor + + def _MoveToDoor_terminal(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> bool: + del memory, params # unused + # Terminate as soon as we are in the doorway. + robot, door = objects + return InDoorway.holds(state, [robot, door]) + + MoveToDoor = ParameterizedOption( + "MoveToDoor", + types=[robot_type, door_type], + # No parameters; the option always moves to the doorway center. + params_space=Box(0, 1, (0, )), + # The policy is a motion planner. + policy=cls._MoveToDoor_policy, + # Only initiable when the robot is in a room for the doory. + initiable=cls._create_move_to_door_initiable(predicates), + terminal=_MoveToDoor_terminal) + + # OpenDoor + def _OpenDoor_policy(_: State, memory: Dict, __: Sequence[Object], + params: Array) -> Action: + del memory # unused + delta_rot = params + return Action(np.array([0.0, 0.0, delta_rot], dtype=np.float32)) + + OpenDoor = SingletonParameterizedOption( + "OpenDoor", + types=[robot_type], + # Even though this option does not need to be parameterized, we + # make it so, because we want to match the parameter space of the + # option that will get learned during option learning. This is + # useful for when we want to use sampler_learner = "oracle" too. + params_space=Box(-np.inf, np.inf, (1, )), + policy=_OpenDoor_policy, + ) + + # MoveThroughDoor + def _MoveThroughDoor_terminal(state: State, memory: Dict, + objects: Sequence[Object], + params: Array) -> bool: + del params # unused + robot, door = objects + target_room = memory["target_room"] + # Sanity check: we should never leave the doorway. + assert InDoorway.holds(state, [robot, door]) + # Terminate as soon as we enter the other room. + return InRoom.holds(state, [ + robot, target_room + ]) or memory["starting_state"].pretty_str() == state.pretty_str() + + MoveThroughDoor = ParameterizedOption( + "MoveThroughDoor", + types=[robot_type, door_type], + # No parameters; the option always moves straight through. + params_space=Box(0, 1, (0, )), + # The policy just moves in a straight line. No motion planning + # required, because there are no obstacles in the doorway. + policy=cls._create_move_through_door_policy(action_space), + # Only initiable when the robot is in the doorway. + initiable=cls._create_move_through_door_initiable(predicates), + terminal=_MoveThroughDoor_terminal) + + return {MoveToDoor, OpenDoor, MoveThroughDoor} + + @classmethod + def _create_move_to_door_initiable( + cls, predicates: Dict[str, Predicate]) -> ParameterizedInitiable: + + InRoom = predicates["InRoom"] + + def initiable(state: State, memory: Dict, objects: Sequence[Object], + params: Array) -> bool: + del params # unused + robot, door = objects + # The robot must be in one of the rooms for the door. + for ro in DoorsEnv.door_to_rooms(door, state): + if InRoom.holds(state, [robot, ro]): + room = ro + break + else: + return False + # Make a plan and store it in memory for use in the policy. Note + # that policies are assumed to be deterministic, but RRT is + # stochastic. We enforce determinism by using a constant seed. + rng = np.random.default_rng(CFG.seed) + + room_rect = DoorsEnv.object_to_geom(room, state) + + def _sample_fn(_: Array) -> Array: + # Sample a point in the room that is far enough away from the + # wall (to save on collision checking). + assert isinstance(room_rect, Rectangle) # pragma: no cover + x_lb = room_rect.x + DoorsEnv.robot_radius # pragma: no cover + x_ub = room_rect.x + DoorsEnv.room_size - \ + DoorsEnv.robot_radius # pragma: no cover + y_lb = room_rect.y + DoorsEnv.robot_radius # pragma: no cover + y_ub = room_rect.y + DoorsEnv.room_size - \ + DoorsEnv.robot_radius # pragma: no cover + x = rng.uniform(x_lb, x_ub) # pragma: no cover + y = rng.uniform(y_lb, y_ub) # pragma: no cover + return np.array([x, y], dtype=np.float32) # pragma: no cover + + def _extend_fn(pt1: Array, pt2: Array) -> Iterator[Array]: + # Make sure that we obey the bounds on actions. + distance = np.linalg.norm(pt2 - pt1) + num = int(distance / DoorsEnv.action_magnitude) + 1 + for i in range(1, num + 1): + yield pt1 * (1 - i / num) + pt2 * i / num + + def _collision_fn(pt: Array) -> bool: + return DoorKnobsEnv.state_has_collision(state, pt) + + def _distance_fn(from_pt: Array, to_pt: Array) -> float: + return np.sum(np.subtract(from_pt, + to_pt)**2) # pragma: no cover + + birrt = utils.BiRRT(_sample_fn, + _extend_fn, + _collision_fn, + _distance_fn, + rng, + num_attempts=CFG.doors_birrt_num_attempts, + num_iters=CFG.doors_birrt_num_iters, + smooth_amt=CFG.doors_birrt_smooth_amt) + + # Set up the initial and target inputs for the motion planner. + robot_x = state.get(robot, "x") + robot_y = state.get(robot, "y") + target_x, target_y = cls._get_position_in_doorway( + room, door, state) + initial_state = np.array([robot_x, robot_y]) + target_state = np.array([target_x, target_y]) + # Run planning. + position_plan = birrt.query(initial_state, target_state) + # In very rare cases, motion planning fails (it is stochastic after + # all). In this case, determine the option to be not initiable. + if position_plan is None: # pragma: no cover + return False + # The position plan is used for the termination check, and for debug + # drawing in the rendering. + memory["position_plan"] = position_plan + # Convert the plan from position space to action space. + deltas = np.subtract(position_plan[1:], position_plan[:-1]) + action_plan = [ + Action(np.array([dx, dy, 0.0], dtype=np.float32)) + for (dx, dy) in deltas + ] + memory["action_plan"] = action_plan + return True + + return initiable + + @classmethod + def _create_move_through_door_initiable( + cls, predicates: Dict[str, Predicate]) -> ParameterizedInitiable: + + InDoorway = predicates["InDoorway"] + InRoom = predicates["InRoom"] + + def initiable(state: State, memory: Dict, objects: Sequence[Object], + params: Array) -> bool: + del params # unused + robot, door = objects + # The robot must be in the doorway. + if not InDoorway.holds(state, [robot, door]): + return False + # The door must be open. + # The option is initiable. Memorize the target -- otherwise, we + # wouldn't know which side of the door to move toward. + room1, room2 = DoorsEnv.door_to_rooms(door, state) + if InRoom.holds(state, [robot, room1]): + end_room = room2 + else: + assert InRoom.holds(state, [robot, room2]) + end_room = room1 + memory["target"] = cls._get_position_in_doorway( + end_room, door, state) + memory["target_room"] = end_room + memory["starting_state"] = state + return True + + return initiable diff --git a/predicators/settings.py b/predicators/settings.py index aecd64f4d1..95384beaf8 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -289,6 +289,10 @@ class GlobalSettings: doors_birrt_smooth_amt = 50 doors_draw_debug = False + # doorknobs env parameters + doorknobs_target_value = 0.75 + test_doors_room_map_size = 10 + # narrow_passage env parameters narrow_passage_open_door_refine_penalty = 0 narrow_passage_door_width_padding_lb = 1e-4 diff --git a/tests/envs/test_doors_env.py b/tests/envs/test_doors_env.py index 39ff11bad6..7add7c7184 100644 --- a/tests/envs/test_doors_env.py +++ b/tests/envs/test_doors_env.py @@ -2,8 +2,10 @@ import numpy as np from predicators import utils -from predicators.envs.doors import DoorsEnv +from predicators.envs.doors import DoorKnobsEnv, DoorsEnv from predicators.ground_truth_models import get_gt_options +from predicators.ground_truth_models.doors.nsrts import \ + DoorknobsGroundTruthNSRTFactory from predicators.structs import Action, EnvironmentTask, GroundAtom, Object, \ State @@ -229,3 +231,211 @@ def _get_delta_rot_to_open_door(door: Object, s: State) -> float: # OpenDoor is not initiable if we're not touching the door. assert not OpenDoor.ground([right_door, robot], [right_door_delta, 1.0]).initiable(state) + + +def test_doorknobs(): + """Tests for DoorKnobsEnv().""" + utils.reset_config({ + "env": "doors", + "doors_room_map_size": 2, + "test_doors_room_map_size": 2, + "doors_min_obstacles_per_room": 1, + "doors_max_obstacles_per_room": 1, + "doors_min_room_exists_frac": 1.0, + "doors_max_room_exists_frac": 1.0, + "doors_draw_debug": True, + }) + env = DoorKnobsEnv() + for task in env.get_train_tasks(): + for obj in task.init: + assert len(obj.type.feature_names) == len(task.init[obj]) + for task in env.get_test_tasks(): + for obj in task.init: + assert len(obj.type.feature_names) == len(task.init[obj]) + assert len(env.predicates) == 5 + DoorInRoom, DoorsShareRoom, InDoorway, InMainRoom, InRoom, \ + = sorted(env.predicates) + assert DoorInRoom.name == "DoorInRoom" + assert DoorsShareRoom.name == "DoorsShareRoom" + assert InDoorway.name == "InDoorway" + assert InMainRoom.name == "InMainRoom" + assert InRoom.name == "InRoom" + assert env.goal_predicates == {InRoom} + options = (get_gt_options(env.get_name())) + assert len(options) == 3 + MoveThroughDoor, MoveToDoor, OpenDoor = sorted( + get_gt_options(env.get_name())) + assert MoveThroughDoor.name == "MoveThroughDoor" + assert MoveToDoor.name == "MoveToDoor" + assert OpenDoor.name == "OpenDoor" + assert len(env.types) == 5 + door_type, knob_type, obstacle_type, robot_type, room_type = sorted( + env.types) + assert door_type.name == "door" + assert knob_type.name == "knob" + assert obstacle_type.name == "obstacle" + assert robot_type.name == "robot" + assert room_type.name == "room" + assert env.action_space.shape == (3, ) + # Create a custom initial state, with all rooms in the 2x2 grid, with the + # robot starting out in the top left, and obstacles in the top right and + # bottom left rooms. + state = env.get_train_tasks()[0].init.copy() + robot, = state.get_objects(robot_type) + rooms = state.get_objects(room_type) + assert len(rooms) == 4 + # Recall that the obstacles include the walls. + expected_num_walls = 24 + obstacles = state.get_objects(obstacle_type) + # Walls + 1 obstacle per room. + assert len(obstacles) == expected_num_walls + len(rooms) + doors = state.get_objects(door_type) + assert len(doors) == 4 + # Remove the obstacle from the top left room. + top_left_obstacles = [o for o in obstacles if "-0-0-obstacle" in o.name] + assert len(top_left_obstacles) == 1 + top_left_obstacle = top_left_obstacles[0] + state = utils.StateWithCache( + {o: state[o] + for o in state if o != top_left_obstacle}, state.cache) + # Put the robot in the middle of the top left room. + top_left_room, top_right_room, _, bottom_right_room = sorted(rooms) + room_cx = state.get(top_left_room, "x") + env.room_size / 2 + room_cy = state.get(top_left_room, "y") + env.room_size / 2 + state.set(robot, "x", room_cx) + state.set(robot, "y", room_cy) + # For later tests, make sure that the obstacle in the top right room is + # exactly in the center. + top_right_obstacles = [o for o in obstacles if "-0-1-obstacle" in o.name] + assert len(top_right_obstacles) == 1 + top_right_obstacle = top_right_obstacles[0] + w = state.get(top_right_obstacle, "width") + h = state.get(top_right_obstacle, "height") + x = state.get(top_right_room, "x") + env.room_size / 2 - w + y = state.get(top_right_room, "y") + env.room_size / 2 - h + state.set(top_right_obstacle, "x", x) + state.set(top_right_obstacle, "y", y) + state.set(top_right_obstacle, "theta", 0.0) + # Since we moved obstacles around, the caches in the original env will be + # wrong. Make a new env to be safe. + old_env = env + env = DoorKnobsEnv() + # types = {"robot": robot_type,"door": door_type, "knob": knob_type, + # "room": room_type, "obstacle": obstacle_type} + types = {t.name: t for t in env.types} + predicates = {p.name: p for p in env.predicates} + options = {o.name: o for o in options} + nsrts = DoorknobsGroundTruthNSRTFactory.get_nsrts\ + ("doorknobs", types,predicates, options) + assert len(nsrts) == 4 + env._door_to_knob = old_env._door_to_knob # pylint: disable=protected-access + # Since we removed the obstacle, there should be no collisions. + assert not env.state_has_collision( + state, + [state.get(robot, "x"), state.get(robot, "y")]) + assert GroundAtom(InRoom, [robot, top_left_room]).holds(state) + assert GroundAtom(InMainRoom, [robot, top_left_room]).holds(state) + # Create a task with a goal to move to the bottom right room. + goal_atom = GroundAtom(InRoom, [robot, bottom_right_room]) + goal = {goal_atom} + task = EnvironmentTask(state, goal) + env.render_state(state, task) + + ## Test simulate ## + + # Test that the robot is contained within the walls when moving in any + # direction, because the doors are initially closed. + for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]: + action = Action(env.action_magnitude * + np.array([dx, dy, 0.0], dtype=np.float32)) + assert env.action_space.contains(action.arr) + s = state.copy() + for _ in range(50): + s = env.simulate(s, action) + # Should still be in the room. + assert GroundAtom(InRoom, [robot, top_left_room]).holds(state) + + # Test opening the door on the right. First, find the door. + top_doors = [d for d in doors \ + if GroundAtom(DoorInRoom, [d, top_left_room]).holds(state) and \ + GroundAtom(DoorInRoom, [d, top_right_room]).holds(state) + ] + assert len(top_doors) == 1 + top_door = top_doors[0] + # Move more than enough steps to touch the door. + action = Action(env.action_magnitude * + np.array([1.0, 0.0, 0.0], dtype=np.float32)) + s = state.copy() + for _ in range(50): + s = env.simulate(s, action) + # The door should start off closed. + # The robot should now be in the doorway and touching the door. + assert GroundAtom(InDoorway, [robot, top_door]).holds(s) + assert not GroundAtom(InMainRoom, [robot, top_left_room]).holds(s) + # Now, open the door. + action = Action(np.array([0.0, 0.0, 0.75], dtype=np.float32)) + s = env.simulate(s, action) + # The door should now be open. + # The robot should still be in the doorway, but not touching the door. + assert GroundAtom(InDoorway, [robot, top_door]).holds(s) + + # Test obstacle collisions. Continuing from the previous state, if we + # move to the right, we should run into the obstacle at the center of + # the room, and not pass it. + action = Action(env.action_magnitude * + np.array([1.0, 0.0, 0.0], dtype=np.float32)) + for _ in range(50): + s = env.simulate(s, action) + # The robot should still be on the left of the obstacle. + obstacle_x = s.get(top_right_obstacle, "x") + robot_x = s.get(robot, "x") + assert robot_x < obstacle_x + + ## Test options ## + + # Find the right door. + right_doors = [d for d in doors \ + if GroundAtom(DoorInRoom, [d, top_right_room]).holds(state) and \ + GroundAtom(DoorInRoom, [d, bottom_right_room]).holds(state) + ] + assert len(right_doors) == 1 + right_door = right_doors[0] + + # Test options working as expected. + + option_plan = [ + MoveToDoor.ground([robot, top_door], []), + OpenDoor.ground([robot], [0.75]), + MoveThroughDoor.ground([robot, top_door], []), + MoveToDoor.ground([robot, right_door], []), + OpenDoor.ground([robot], [0.75]), + MoveThroughDoor.ground([robot, right_door], []), + ] + policy = utils.option_plan_to_policy(option_plan) + traj = utils.run_policy_with_simulator( + policy, + env.simulate, + task.init, + lambda _: False, + max_num_steps=1000, + exceptions_to_break_on={utils.OptionExecutionFailure}) + assert goal_atom.holds(traj.states[-1]) + # Cover a few additional rendering cases. + env.render_state(traj.states[0], task, traj.actions[0]) + env.render_state(traj.states[-1], task) + # Test if MoveToDoor is initiable when in same room as door. + assert MoveToDoor.ground([robot, top_door], []).initiable(state) + + # Test options in cases where they are not initiable. + # MoveToDoor is not initiable if we're not already in the room. + assert not MoveToDoor.ground([robot, right_door], []).initiable(state) + + # MoveThroughDoor is not initiable if we're not in the doorway. + assert not MoveThroughDoor.ground([robot, top_door], []).initiable(state) + # or if we're in the doorway, but the door isn't open. + action = Action(env.action_magnitude * + np.array([1.0, 0.0, 0.0], dtype=np.float32)) + s = state.copy() + for _ in range(50): + s = env.simulate(s, action) + assert GroundAtom(InDoorway, [robot, top_door]).holds(s) From 8df9bda3f6a2dda008ea0f738af387567bec1d6d Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Sat, 27 Jul 2024 11:25:03 -0700 Subject: [PATCH 29/38] LLM-based operator learning (#1690) * making slow progress * making good progress; almost there! * good to go! * feature-complete! * good to go * fix checks * fix tests --- .../naive_no_examples.txt | 64 +++++ .../strips_learning/llm_strips_learner.py | 241 ++++++++++++++++++ predicators/structs.py | 6 + tests/envs/test_doors_env.py | 3 +- .../test_llm_strips_learner.py | 71 ++++++ 5 files changed, 384 insertions(+), 1 deletion(-) create mode 100644 predicators/nsrt_learning/strips_learning/llm_op_learning_prompts/naive_no_examples.txt create mode 100644 predicators/nsrt_learning/strips_learning/llm_strips_learner.py create mode 100644 tests/nsrt_learning/strips_learning/test_llm_strips_learner.py diff --git a/predicators/nsrt_learning/strips_learning/llm_op_learning_prompts/naive_no_examples.txt b/predicators/nsrt_learning/strips_learning/llm_op_learning_prompts/naive_no_examples.txt new file mode 100644 index 0000000000..3c9cdf825e --- /dev/null +++ b/predicators/nsrt_learning/strips_learning/llm_op_learning_prompts/naive_no_examples.txt @@ -0,0 +1,64 @@ +You are a system whose job is to learn PDDL-style operators given predicates and trajectory data. Specifically, given a list of predicates and types, as well as trajectories consisting states and actions, where every state is a set of true ground predicates (assume all predicates not mentioned in the state are false) and every action has a name and takes in particular objects, propose as many symbolic operators that might help model these states as you can. These operators will be used to plan on similar tasks. +An operator should have the form (note that the angle brackets here are just for clarifying the syntax; do not output angle brackets in your responses): +(, , ..., ) +preconditions: +add effects: +delete effects: +action: + + +Please structure your output in the following format (once again, angle brackets are just for syntax-clarification): +``` +Operators: + + + +... + +``` + + +Here's an example for a simple block-stacking domain. +Input: +``` +Types: +- block + +Predicates: +- (OnTable ?b0 - block) +- (Clear ?b0 - block) +- (On ?b0 - block ?b1 - block) +- (Handempty) + +Actions: +- Pickup(?b - block) +- PutDown(?b - block) + +Trajectory data: +Trajectory 0 (Goal: {Clear(A)}): +State 0: {OnTable(A), On(B, A), Handempty(), Clear(B), OnTable(B)} +Action 0: Pickup(B) +State 1: {Holding(B), OnTable(A), Clear(A)} +Action 1: PutDown(B) +State 2: {OnTable(A), OnTable(B), Handempty(), Clear(A), Clear(B)} +``` + +Expected Output: +``` +Operators: +PickupOp0(?b0 - block) +preconditions: (and (Clear ?b0) (Handempty) (OnTable ?b0)) +add effects: (and (Holding ?b0)) +delete effects: (and (Handempty) (OnTable ?b0) (Clear ?b0)) +action: Pickup(?b0) + +PlaceDownOp0(?b0 - block) +preconditions: (and (Holding ?b0)) +add effects: (and (Clear ?b0) (Handempty) (OnTable ?b0)) +delete effects: (and (Holding ?b0)) +action: Pickup(?b0) +``` +Explanation: +1. **PickUpOp0**: This operator models the action of picking up a block. It requires that the robot's hand be empty and the block be on table, and causes the robot to be holding the block. + +2. **PlaceDownOp0**: This operator models the action of placing down a block on the table. It requires that the robot is holding a block, and causes that block to be on the table, and the orbot's hand to be empty. diff --git a/predicators/nsrt_learning/strips_learning/llm_strips_learner.py b/predicators/nsrt_learning/strips_learning/llm_strips_learner.py new file mode 100644 index 0000000000..81a8209573 --- /dev/null +++ b/predicators/nsrt_learning/strips_learning/llm_strips_learner.py @@ -0,0 +1,241 @@ +"""Approaches that use an LLM to learn STRIPS operators instead of performing +symbolic learning of any kind.""" + +import re +from typing import Any, Dict, List, Optional, Set, Tuple + +from predicators import utils +from predicators.nsrt_learning.strips_learning import BaseSTRIPSLearner +from predicators.settings import CFG +from predicators.structs import PNAD, LiftedAtom, LowLevelTrajectory, \ + ParameterizedOption, Predicate, Segment, STRIPSOperator, Task, Type, \ + Variable + + +class LLMStripsLearner(BaseSTRIPSLearner): + """Base class for all LLM-based learners.""" + + def __init__(self, + trajectories: List[LowLevelTrajectory], + train_tasks: List[Task], + predicates: Set[Predicate], + segmented_trajs: List[List[Segment]], + verify_harmlessness: bool, + annotations: Optional[List[Any]], + verbose: bool = True) -> None: + super().__init__(trajectories, train_tasks, predicates, + segmented_trajs, verify_harmlessness, annotations, + verbose) + self._llm = utils.create_llm_by_name(CFG.llm_model_name) + prompt_file = utils.get_path_to_predicators_root() + \ + "/predicators/nsrt_learning/strips_learning/" + \ + "llm_op_learning_prompts/naive_no_examples.txt" + with open(prompt_file, "r", encoding="utf-8") as f: + self._base_prompt = f.read() + self._name_to_pred = {p.name: p for p in self._predicates} + + def _get_all_types_from_preds(self) -> Set[Type]: + all_types: Set[Type] = set() + for pred in self._predicates: + all_types |= set(pred.types) + return all_types + + def _get_all_options_from_segs(self) -> Set[ParameterizedOption]: + # NOTE: this assumes all segments in self._segmented_trajs + # have options associated with them. + all_options: Set[ParameterizedOption] = set() + for seg_traj in self._segmented_trajs: + for seg in seg_traj: + all_options.add(seg.get_option().parent) + return all_options + + def _parse_line_into_pred_names_and_arg_names( + self, line_str: str) -> List[Tuple[str, List[str]]]: + pattern = r'\(\s*([a-zA-Z_][a-zA-Z0-9_]*)\s*([^\(\)]*)\)' + matches = re.findall(pattern, line_str) + predicates = [] + for match in matches: + predicate = match[0] + arguments = [ + arg.strip() for arg in match[1].split() if arg.strip() + ] + predicates.append((predicate, arguments)) + return predicates + + def _parse_option_str_into_opt_name_and_arg_names( + self, line_str: str) -> Tuple[str, List[str]]: + match = re.match(r'action: (\w+)\(([^)]*)\)', line_str) + if not match: # pragma: no cover + raise ValueError("The input string is not in the expected format.") + skill_name = match.group(1) + args = match.group(2).split(',') if match.group(2) else [] + args = [arg.strip() for arg in args] + return skill_name, args + + def _parse_operator_str_into_structured_elems( + self, op_str: str + ) -> Tuple[str, Dict[str, str], List[Tuple[str, List[str]]], List[Tuple[ + str, List[str]]], List[Tuple[str, List[str]]], Tuple[str, + List[str]]]: + op_str_elems = op_str.split('\n') + # Parse out operator name and args. + name_and_args = op_str_elems[0] + opening_paren_loc = name_and_args.find("(") + closing_paren_loc = name_and_args.find(")") + name_str = name_and_args[:opening_paren_loc] + arg_str = name_and_args[opening_paren_loc + 1:closing_paren_loc] + args = arg_str.split() + arg_dict = {} + for i in range(0, len(args), 3): + arg_name = args[i] + arg_type = args[i + 2] + arg_dict[arg_name] = arg_type + # Parse out structures for preconditions and effects. + structured_precs = self._parse_line_into_pred_names_and_arg_names( + op_str_elems[1]) + structured_add_effs = self._parse_line_into_pred_names_and_arg_names( + op_str_elems[2]) + structured_del_effs = self._parse_line_into_pred_names_and_arg_names( + op_str_elems[3]) + structured_action = self._parse_option_str_into_opt_name_and_arg_names( + op_str_elems[4]) + return (name_str, arg_dict, structured_precs, structured_add_effs, + structured_del_effs, structured_action) + + def _convert_structured_precs_or_effs_into_lifted_atom_set( + self, structured_precs_or_effs: List[Tuple[str, List[str]]], + pred_name_to_pred: Dict[str, Predicate], + op_var_name_to_op_var: Dict[str, Variable]) -> Set[LiftedAtom]: + ret_atoms = set() + for prec_name, prec_args_list in structured_precs_or_effs: + if prec_name not in pred_name_to_pred: + continue # pragma: no cover + all_args_valid = True + prec_arg_vars = [] + for prec_arg in prec_args_list: + if prec_arg not in op_var_name_to_op_var: # pragma: no cover + all_args_valid = False + break + prec_arg_vars.append(op_var_name_to_op_var[prec_arg]) + if not all_args_valid: + continue # pragma: no cover + ret_atoms.add( + LiftedAtom(pred_name_to_pred[prec_name], prec_arg_vars)) + return ret_atoms + + # NOTE: we actually do test this function, but the many sub-cases + # etc. make it annoying to test, so we pragma: no cover the + # whole thing. + def _learn(self) -> List[PNAD]: # pragma: no cover + """Overview of what's going on here: + + 1. Form a prompt that contains the object types, predicates, and + options in this env, as well as the training demonstration + trajectories. + 2. Query an LLM to return operators given the above information + from (1). + 3. Parse out symbolic operators from the LLM's response, with automatic + pruning of nonsensical/hallucinated operators. This is sufficient + info to make PNADs with empty datastores. + 4. Associate data with each PNAD via our + _find_best_matching_pnad_and_sub procedure; this populates the + PNAD's datastore. + """ + prompt = self._base_prompt + all_types = self._get_all_types_from_preds() + all_options = sorted(self._get_all_options_from_segs()) + type_name_to_type = {t.name: t for t in all_types} + pred_name_to_pred = {p.name: p for p in self._predicates} + option_name_to_option = {o.name: o for o in all_options} + prompt += "Types:\n" + for t in sorted(all_types): + prompt += f"- {t.name}\n" + prompt += "\nPredicates:\n" + for pred in sorted(self._predicates): + prompt += f"- {pred.pddl_str()}\n" + prompt += "\nActions:\n" + for act in all_options: + prompt += act.pddl_str() + "\n" + prompt += "\nTrajectory data:\n" + for i, seg_traj in enumerate(self._segmented_trajs): + curr_goal = self._train_tasks[i].goal + prompt += f"Trajectory {i} (Goal: {str(curr_goal)}):\n" + for timestep, seg in enumerate(seg_traj): + prompt += f"State {timestep}: {str(seg.init_atoms)}\n" + curr_option = seg.get_option() + action = curr_option.name + "(" + ", ".join( + obj.name for obj in curr_option.objects) + ")" + prompt += f"Action {timestep}: {action}" + prompt += f"State {timestep + 1}: {str(seg.final_atoms)}" # pylint:disable=undefined-loop-variable + prompt += "\n" + # Query the LLM to see what kinds of operators it proposes. + llm_response = self._llm.sample_completions(prompt, None, 0.0, + CFG.seed) + op_predictions = llm_response[0] + pattern = r'```\n(.*?)\n```' + matches = re.findall(pattern, op_predictions, re.DOTALL) + assert matches[0][:10] == "Operators:" + specific_op_prediction = matches[0][11:].split('\n\n') + # Now parse the operators into actual structured symbolic operators + # with associated option specs (i.e. PNADs, but without the + # datastores set). + pnads = [] + for op_pred in specific_op_prediction: + incorrect_op = False + name_str, arg_dict, structured_precs, structured_add_effs, \ + structured_del_effs, structured_action = \ + self._parse_operator_str_into_structured_elems(op_pred) + op_var_name_to_op_var = {} + for arg_name_str, arg_type_str in arg_dict.items(): + if arg_type_str not in type_name_to_type: + incorrect_op = True + break + op_var_name_to_op_var[arg_name_str] = Variable( + arg_name_str, type_name_to_type[arg_type_str]) + if incorrect_op: + continue + preconditions = \ + self._convert_structured_precs_or_effs_into_lifted_atom_set( + structured_precs, pred_name_to_pred, op_var_name_to_op_var) + add_effs = \ + self._convert_structured_precs_or_effs_into_lifted_atom_set( + structured_add_effs, pred_name_to_pred, op_var_name_to_op_var) + del_effs = \ + self._convert_structured_precs_or_effs_into_lifted_atom_set( + structured_del_effs, pred_name_to_pred, op_var_name_to_op_var) + option_name_str, option_args_strs_list = structured_action + if option_name_str not in option_name_to_option: + continue + option = option_name_to_option[option_name_str] + option_arg_vars = [] + for option_arg_str in option_args_strs_list: + if option_arg_str not in op_var_name_to_op_var: + incorrect_op = True + break + option_arg_vars.append(op_var_name_to_op_var[option_arg_str]) + if incorrect_op: + continue + # NOTE: for now, we do not create operators with ignore effects! + # This could be done in the future by modifying the LLM query + # prompt. + symbolic_op = STRIPSOperator(name_str, + list(op_var_name_to_op_var.values()), + preconditions, add_effs, del_effs, + set()) + pnad = PNAD(symbolic_op, [], (option, option_arg_vars)) + pnads.append(pnad) + # Finally, populate the datastore of each PNAD. + curr_pnad: Optional[PNAD] = None + for seg_traj in self._segmented_trajs: + for seg in seg_traj: + objects = set(seg.states[0]) + curr_pnad, var_to_obj_sub = \ + self._find_best_matching_pnad_and_sub(seg, objects, pnads) + if curr_pnad is not None: + assert var_to_obj_sub is not None + curr_pnad.add_to_datastore((seg, var_to_obj_sub)) + return pnads + + @classmethod + def get_name(cls) -> str: + return "llm" diff --git a/predicators/structs.py b/predicators/structs.py index cf703f2e3f..d058feae98 100644 --- a/predicators/structs.py +++ b/predicators/structs.py @@ -593,6 +593,12 @@ def ground(self, objects: Sequence[Object], params: Array) -> _Option: params=params, memory=memory) + def pddl_str(self) -> str: + """Turn this option into a string that is PDDL-like.""" + params_str = " ".join(f"?x{i} - {t.name}" + for i, t in enumerate(self.types)) + return f"{self.name}({params_str})" + @dataclass(eq=False) class _Option: diff --git a/tests/envs/test_doors_env.py b/tests/envs/test_doors_env.py index 7add7c7184..4b82a7e982 100644 --- a/tests/envs/test_doors_env.py +++ b/tests/envs/test_doors_env.py @@ -236,7 +236,7 @@ def _get_delta_rot_to_open_door(door: Object, s: State) -> float: def test_doorknobs(): """Tests for DoorKnobsEnv().""" utils.reset_config({ - "env": "doors", + "env": "doorknobs", "doors_room_map_size": 2, "test_doors_room_map_size": 2, "doors_min_obstacles_per_room": 1, @@ -325,6 +325,7 @@ def test_doorknobs(): types = {t.name: t for t in env.types} predicates = {p.name: p for p in env.predicates} options = {o.name: o for o in options} + assert DoorknobsGroundTruthNSRTFactory.get_env_names() == {"doorknobs"} nsrts = DoorknobsGroundTruthNSRTFactory.get_nsrts\ ("doorknobs", types,predicates, options) assert len(nsrts) == 4 diff --git a/tests/nsrt_learning/strips_learning/test_llm_strips_learner.py b/tests/nsrt_learning/strips_learning/test_llm_strips_learner.py new file mode 100644 index 0000000000..5316130117 --- /dev/null +++ b/tests/nsrt_learning/strips_learning/test_llm_strips_learner.py @@ -0,0 +1,71 @@ +"""Tests for methods in the BaseSTRIPSLearner class.""" + +from predicators import utils +from predicators.datasets import create_dataset +from predicators.envs import create_new_env +from predicators.ground_truth_models import get_gt_options +from predicators.nsrt_learning.segmentation import segment_trajectory +from predicators.nsrt_learning.strips_learning.llm_strips_learner import \ + LLMStripsLearner +from predicators.pretrained_model_interface import LargeLanguageModel + + +class _DummyLLM(LargeLanguageModel): + + def get_id(self): + return "dummy" + + def _sample_completions(self, + prompt, + imgs, + temperature, + seed, + stop_token=None, + num_completions=1): + del imgs # unused. + completions = [] + for _ in range(num_completions): + # Hardcoded completion for 'cover'. + completion = """``` +Operators: +PickPlaceOp1(?b - block ?t - target) +preconditions: (and (Holding ?b) (IsTarget ?t)) +add effects: (and (Covers ?b ?t) (HandEmpty)) +delete effects: (and (Holding ?b)) +action: PickPlace() + +PickPlaceOp2(?b - block) +preconditions: (and (HandEmpty) (IsBlock ?b)) +add effects: (and (Holding ?b)) +delete effects: (and (HandEmpty)) +action: PickPlace() +``` +""" + completions.append(completion) + return completions + + +def test_llm_op_learning(): + """Test the operator learning approach using a dummy llm.""" + utils.reset_config({ + "env": "cover", + "strips_learner": "llm", + "num_train_tasks": 3 + }) + env = create_new_env("cover") + train_tasks = [t.task for t in env.get_train_tasks()] + predicates, _ = utils.parse_config_excluded_predicates(env) + dataset = create_dataset(env, train_tasks, get_gt_options(env.get_name()), + predicates) + segmented_trajs = [ + segment_trajectory(t, env.predicates) for t in dataset.trajectories + ] + learner = LLMStripsLearner(dataset.trajectories, + train_tasks, + env.predicates, + segmented_trajs, + verify_harmlessness=True, + annotations=None) + learner._llm = _DummyLLM() # pylint:disable=protected-access + pnads = learner._learn() # pylint:disable=protected-access + assert len(pnads) == 2 From d312e96a2d85fb4cb38449dc076c17a18af94e2b Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Sat, 27 Jul 2024 23:37:01 -0700 Subject: [PATCH 30/38] gtg (#1695) --- scripts/run_checks.sh | 2 +- .../strips_learning/test_llm_strips_learner.py | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/scripts/run_checks.sh b/scripts/run_checks.sh index 8e2beca2b6..5908ba8f96 100755 --- a/scripts/run_checks.sh +++ b/scripts/run_checks.sh @@ -27,7 +27,7 @@ else fi echo "Running unit tests." -pytest -s tests/ --cov-config=.coveragerc --cov=predicators/ --cov=tests/ --cov-fail-under=100 --cov-report=term-missing:skip-covered --durations=10 +pytest -s tests/ --cov-config=.coveragerc --cov=predicators/ --cov=tests/ --cov-fail-under=100 --cov-report=term-missing:skip-covered --durations=0 if [ $? -eq 0 ]; then echo "Unit tests passed." else diff --git a/tests/nsrt_learning/strips_learning/test_llm_strips_learner.py b/tests/nsrt_learning/strips_learning/test_llm_strips_learner.py index 5316130117..8c294db2d1 100644 --- a/tests/nsrt_learning/strips_learning/test_llm_strips_learner.py +++ b/tests/nsrt_learning/strips_learning/test_llm_strips_learner.py @@ -1,5 +1,8 @@ """Tests for methods in the BaseSTRIPSLearner class.""" +import glob +import shutil + from predicators import utils from predicators.datasets import create_dataset from predicators.envs import create_new_env @@ -8,6 +11,7 @@ from predicators.nsrt_learning.strips_learning.llm_strips_learner import \ LLMStripsLearner from predicators.pretrained_model_interface import LargeLanguageModel +from predicators.settings import CFG class _DummyLLM(LargeLanguageModel): @@ -52,6 +56,10 @@ def test_llm_op_learning(): "strips_learner": "llm", "num_train_tasks": 3 }) + # start by flushing the pretrained models cache! + for f in glob.glob(CFG.pretrained_model_prompt_cache_dir + + "/dummy_most_likely*"): + shutil.rmtree(f) env = create_new_env("cover") train_tasks = [t.task for t in env.get_train_tasks()] predicates, _ = utils.parse_config_excluded_predicates(env) From 1ccf251f82f78b198335deeb96e0ece5393455d6 Mon Sep 17 00:00:00 2001 From: Bowen Li <56875617+Jaraxxus-Me@users.noreply.github.com> Date: Sun, 28 Jul 2024 15:26:31 -0400 Subject: [PATCH 31/38] Bowen gnn (#1696) * modified gnn option * formatting * type def * all checks passed --- .../approaches/gnn_option_policy_approach.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/predicators/approaches/gnn_option_policy_approach.py b/predicators/approaches/gnn_option_policy_approach.py index 7ecd84b6cd..a001e4632e 100644 --- a/predicators/approaches/gnn_option_policy_approach.py +++ b/predicators/approaches/gnn_option_policy_approach.py @@ -199,7 +199,12 @@ def _solve_with_shooting(self, task: Task, start_time = time.perf_counter() memory: Dict = {} # optionally updated by predict() # Keep trying until the timeout. + tries: int = 0 + all_num_act: int = 0 + total_num_act: int = 0 while time.perf_counter() - start_time < timeout: + tries += 1 + all_num_act += total_num_act total_num_act = 0 state = task.init plan: List[_Option] = [] @@ -243,10 +248,13 @@ def _policy(s: State) -> Action: break # If num_act is zero, that means that the option is stuck in # the state, so we should break to avoid infinite loops. - if num_act == 0: - break - total_num_act += num_act # Break early if we have timed out. - if time.perf_counter() - start_time < timeout: + total_num_act += num_act + if time.perf_counter() - start_time > timeout: + break + if num_act == 0: break + all_num_act += total_num_act + print(f"Shooting: {all_num_act} actions with {tries} tries in \ + {time.perf_counter() - start_time} seconds") raise ApproachTimeout("Shooting timed out!") From 74d20fe7083617ea86fb4c68a754beb5709935fc Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Thu, 1 Aug 2024 09:03:23 -0700 Subject: [PATCH 32/38] minor (#1698) --- scripts/openstack/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/openstack/README.md b/scripts/openstack/README.md index bf224a4cd2..eb3335233c 100644 --- a/scripts/openstack/README.md +++ b/scripts/openstack/README.md @@ -3,7 +3,7 @@ 1. Request an account through the LIS group. See [here](https://tig.csail.mit.edu/shared-computing/open-stack/quick-start/). 2. Follow the [instructions for creating and uploading a private key](https://tig.csail.mit.edu/shared-computing/open-stack/openstack-ssh-key/). 3. Launch instances using the `predicators` image and your private key. Make sure to enable `allow_incoming_ssh` in the security settings when launching the instance so that the machine is actually accessible. Make sure you launch enough instances so that each (environment, approach, seed) can have one instance. - 1. You might need to create a new image. You can do this by launching an instance, ssh'ing it, installing everything you need, and then using the `Create Snapshot` Action on the instances dashboard. + 1. You might need to create a new image. You can do this by launching an instance, ssh'ing it, installing everything you need, and then using the `Create Snapshot` Action on the instances dashboard. To ssh a machine, find its IP on openstack, and do `ssh ubuntu@` (be sure you're on the MIT network)! 4. Create a file (e.g. `machines.txt`) that lists your instance IP addresses, one per line. Do not include anything on the line that isn't the IP address. 5. Create an experiment yaml file (see `scripts/openstack/configs/example_basic.yaml` for an example). 6. Run `python scripts/openstack/launch.py --config --machines --sshkey ` to launch your experiments. From 3cb4d6d41411bb2b1bd052ec081d12a71b5929a6 Mon Sep 17 00:00:00 2001 From: Yichao Liang Date: Sat, 3 Aug 2024 18:39:32 +0100 Subject: [PATCH 33/38] Update LLMOpenLoopApproach so it can also use Gemini models (#1700) * Add compatibility for LLMOpenLoopApproach to use GEMINI --------- Co-authored-by: yichao-liang --- .gitignore | 5 +++-- predicators/approaches/llm_open_loop_approach.py | 13 +++++++++++-- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index 6018f0798e..166d256c82 100644 --- a/.gitignore +++ b/.gitignore @@ -15,12 +15,13 @@ pylint_recursive.py .coverage results eval_trajectories +images videos -logs +logs* saved_approaches saved_datasets scripts/results -pretrained_model_cache +pretrained_model_cache* tests/datasets/mock_vlm_datasets/cache/ machines.txt *_vision_data diff --git a/predicators/approaches/llm_open_loop_approach.py b/predicators/approaches/llm_open_loop_approach.py index d438420e67..d16b4cdcb1 100644 --- a/predicators/approaches/llm_open_loop_approach.py +++ b/predicators/approaches/llm_open_loop_approach.py @@ -9,6 +9,15 @@ --num_test_tasks 1 \ --debug +Example using Gemini: + export GOOGLE_API_KEY= + python predicators/main.py --approach llm_open_loop --seed 0 \ + --strips_learner oracle \ + --env pddl_blocks_procedural_tasks \ + --num_train_tasks 3 \ + --num_test_tasks 1 \ + --debug --llm_model_name gemini-1.5-flash + Easier setting: python predicators/main.py --approach llm_open_loop --seed 0 \ --strips_learner oracle \ @@ -39,10 +48,10 @@ from predicators.approaches.nsrt_metacontroller_approach import \ NSRTMetacontrollerApproach from predicators.planning import task_plan_with_option_plan_constraint -from predicators.pretrained_model_interface import OpenAILLM from predicators.settings import CFG from predicators.structs import Box, Dataset, GroundAtom, Object, \ ParameterizedOption, Predicate, State, Task, Type, _GroundNSRT, _Option +from predicators.utils import create_llm_by_name class LLMOpenLoopApproach(NSRTMetacontrollerApproach): @@ -54,7 +63,7 @@ def __init__(self, initial_predicates: Set[Predicate], super().__init__(initial_predicates, initial_options, types, action_space, train_tasks) # Set up the LLM. - self._llm = OpenAILLM(CFG.llm_model_name) + self._llm = create_llm_by_name(CFG.llm_model_name) # Set after learning. self._prompt_prefix = "" From 0bb4c1678b2f30a873f517de25d8e076fe636708 Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Sat, 3 Aug 2024 10:46:26 -0700 Subject: [PATCH 34/38] modify codeowners to stop bothering tom (#1701) --- .github/CODEOWNERS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 3b2a7b7438..ae3bb78011 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,2 +1,2 @@ # Default owners for all files. -* @tomsilver +* @NishanthJKumar From 57b3ab0efba5fef422aadf59c0f81f0054c05fc5 Mon Sep 17 00:00:00 2001 From: Yichao Liang Date: Sat, 3 Aug 2024 19:30:54 +0100 Subject: [PATCH 35/38] Introducing an INTERPRET-Inspired Baseline for VLM predicate (#1699) * draft generate dataset with vision api. use with flat --vlm_predicate_vision_api_generate_ground_atoms True * vision api baseline running * fix pylint * works with multiple demos * yapf" * fix mypy * improved regex * uncomment sample * restore accdentally deleted files and changes to docstrings * fix pylint * add test for lfd-vision-api * add pylint: disable=exec-used * yapf * add prompt file for pytest * fix tests * yapf * pylint style * git add tests * yapf * fix test * refactor * yapf --------- Co-authored-by: yichao-liang --- .gitignore | 2 + .../datasets/generate_atom_trajs_with_vlm.py | 184 +++++++++++++++++- .../vision_api/api_oo_state.txt | 138 +++++++++++++ .../vision_api/api_sym_predicate.txt | 26 +++ .../vision_api/prompt.outline | 27 +++ predicators/envs/burger.py | 3 - predicators/envs/cover.py | 12 +- predicators/envs/kitchen.py | 1 + predicators/settings.py | 1 + predicators/structs.py | 37 +++- tests/datasets/test_datasets.py | 60 +++++- 11 files changed, 468 insertions(+), 23 deletions(-) create mode 100644 predicators/datasets/vlm_input_data_prompts/vision_api/api_oo_state.txt create mode 100644 predicators/datasets/vlm_input_data_prompts/vision_api/api_sym_predicate.txt create mode 100644 predicators/datasets/vlm_input_data_prompts/vision_api/prompt.outline diff --git a/.gitignore b/.gitignore index 166d256c82..74eeb654f3 100644 --- a/.gitignore +++ b/.gitignore @@ -30,6 +30,8 @@ tests/_fake_results sas_plan downward/ Gymnasium-Robotics/ +predicators/datasets/vlm_input_data_prompts/vision_api/prompt.txt +predicators/datasets/vlm_input_data_prompts/vision_api/response.txt # Jetbrains IDEs .idea/ diff --git a/predicators/datasets/generate_atom_trajs_with_vlm.py b/predicators/datasets/generate_atom_trajs_with_vlm.py index 2affe19e0c..fb71ae4ecd 100644 --- a/predicators/datasets/generate_atom_trajs_with_vlm.py +++ b/predicators/datasets/generate_atom_trajs_with_vlm.py @@ -5,10 +5,14 @@ import logging import os import re +import textwrap +import traceback from concurrent.futures import ThreadPoolExecutor from functools import partial +from inspect import getsource from pathlib import Path -from typing import Dict, Iterator, List, Optional, Sequence, Set, Tuple, cast +from typing import Dict, Iterator, List, Match, Optional, Sequence, Set, \ + Tuple, cast import dill as pkl import numpy as np @@ -673,7 +677,7 @@ def _parse_vlmtraj_file_into_structured_trajs( return (output_state_trajs, output_action_trajs) -def _query_vlm_to_generate_ground_atoms_trajs( +def _generate_ground_atoms_with_vlm_pure_visual_preds( image_option_trajs: List[ImageOptionTrajectory], env: BaseEnv, train_tasks: List[Task], known_predicates: Set[Predicate], all_task_objs: Set[Object], @@ -735,6 +739,171 @@ def _query_vlm_to_generate_ground_atoms_trajs( return ground_atoms_trajs +def _generate_ground_atoms_with_vlm_oo_code_gen( + image_option_trajs: List[ImageOptionTrajectory], env: BaseEnv, + train_tasks: List[Task], known_predicates: Set[Predicate], + all_task_objs: Set[Object], + vlm: VisionLanguageModel) -> List[List[Set[GroundAtom]]]: + """Given a collection of ImageOptionTrajectories, query a VLM to generate + predicates over the object features, then generate a ground atom + trajectory.""" + del all_task_objs # Unused. + candidates = set() + for io_traj in image_option_trajs: + # 1. Create a prompt based on the image option trajectory. + prompt, imgs = _create_prompt_from_image_option_traj(io_traj, env) + + # 2. Query the VLM to propose predicates. + response = vlm.sample_completions(prompt, + imgs, + 0.0, + CFG.seed, + num_completions=1)[0] + + # 3. Parse the responses into a set of predicates + candidates |= _parse_predicate_proposals(response, train_tasks, env) + + # 4. Generate the ground atom trajectories from the predicates. + ground_atoms_trajs = [] + for image_option_traj in image_option_trajs: + ground_atoms_traj = [] + assert image_option_traj.states is not None + for state in image_option_traj.states: + ground_atoms = utils.abstract(state, candidates | known_predicates) + ground_atoms_traj.append(ground_atoms) + ground_atoms_trajs.append(ground_atoms_traj) + return ground_atoms_trajs + + +def add_python_quote(text: str) -> str: + """Add python quotes to a string.""" + return f"```python\n{text}\n```" + + +def _create_prompt_from_image_option_traj( + image_option_traj: ImageOptionTrajectory, + env: BaseEnv) -> Tuple[str, List[PIL.Image.Image]]: + """Given an image option trajectory, create a prompt for the VLM.""" + prompt_dir = "predicators/datasets/vlm_input_data_prompts/vision_api/" + with open(prompt_dir + "prompt.outline", "r", encoding="utf-8") as f: + template = f.read() + + # Predicate, State API + with open(prompt_dir + 'api_oo_state.txt', 'r', encoding="utf-8") as f: + state_str = f.read() + with open(prompt_dir + 'api_sym_predicate.txt', 'r', + encoding="utf-8") as f: + pred_str = f.read() + + template = template.replace( + '[STRUCT_DEFINITION]', add_python_quote(state_str + '\n\n' + pred_str)) + + # Object types + type_instan_str = _env_type_str(getsource(env.__class__)) + type_instan_str = add_python_quote(type_instan_str) + template = template.replace("[TYPES_IN_ENV]", type_instan_str) + + # Demo + demo_str = [] + imgs = [img_list[0] for img_list in image_option_traj.imgs] + assert image_option_traj.states is not None + for i, a in enumerate(image_option_traj.actions): + state = image_option_traj.states[i] + demo_str.append(f"state {i}:") + demo_str.append(state.dict_str(indent=2, object_features=True)) + demo_str.append(f"action {i}: {a.name}") + num_states = len(image_option_traj.states) + state = image_option_traj.states[-1] + demo_str.append(f"state {num_states}:") + demo_str.append(state.dict_str(indent=2, object_features=True)) + demo_str_ = '\n'.join(demo_str) + template = template.replace("[DEMO_TRAJECTORY]", demo_str_) + + with open(prompt_dir + 'prompt.txt', 'w', encoding="utf-8") as f: + f.write(template) + + return template, imgs + + +import_str = """ +import numpy as np +from typing import Sequence +from predicators.structs import State, Object, Predicate, Type +""" + + +def _env_type_str(source_code: str) -> str: + """Extract the type definitions from the environment source code. + + Requires the types be defined class variabled under `# Types` as in + cover, burger and kitchen. + """ + type_pattern = r"( # Types.*?)(?=\n\s*\n|$)" + type_block = re.search(type_pattern, source_code, re.DOTALL) + if type_block is not None: + type_init_str = type_block.group() + type_init_str = textwrap.dedent(type_init_str) + # type_init_str = add_python_quote(type_init_str) + return type_init_str + raise Exception("No type definitions found in the env") # pragma: no cover + + +def _parse_predicate_proposals( + response: str, + tasks: List[Task], + env: BaseEnv, +) -> Set[Predicate]: + """Parse the prediction file to extract the proposed predicates.""" + # Regular expression to match Python code blocks + pattern = re.compile(r'```python(.*?)```', re.DOTALL) + python_blocks = [] + # Find all Python code blocks in the text + for match_block in pattern.finditer(response): + # Extract the Python code block and add it to the list + python_blocks.append(match_block.group(1).strip()) + + candidates = set() + context: Dict = {} + + env_source_code = getsource(env.__class__) + type_init_str = _env_type_str(env_source_code) + # constants_str = self._constants_str(self.env_source_code) + # pylint: disable=exec-used + # Disabling exec-used warning because pylint dislike exec + exec(import_str, context) + exec(type_init_str, context) + # pylint: enable=exec-used + + for code_str in python_blocks: + # Extract name from code block + match: Optional[Match[str]] = re.search( + r'(\w+)(: Predicate)?\s*=\s*Predicate', code_str) + if match is None: + logging.warning("No predicate name found in the code block") + continue + pred_name = match.group(1) + logging.info(f"Found definition for predicate {pred_name}") + + # Try to check if it's roughly runable. And only add it to + # our list if it is. + try: + # pylint: disable=exec-used + exec(code_str, context) + # pylint: enable=exec-used + utils.abstract(tasks[0].init, [context[pred_name]]) + except (TypeError, AttributeError, ValueError) as e: + # Was using Exception but pylint was complaining, so I'm + # adding specific exceptions to this tuple as we encounter them. + error_trace = traceback.format_exc() + logging.warning(f"Proposed predicate {pred_name} not " + f"executable: {e}\n{error_trace}") + continue + else: + candidates.add(context[pred_name]) + + return candidates + + def create_ground_atom_data_from_generated_demos( dataset: Dataset, env: BaseEnv, @@ -816,9 +985,12 @@ def create_ground_atom_data_from_generated_demos( # Now, given these trajectories, we can query the VLM. if vlm is None: vlm = utils.create_vlm_by_name(CFG.vlm_model_name) # pragma: no cover - ground_atoms_trajs = _query_vlm_to_generate_ground_atoms_trajs( - img_option_trajs, env, train_tasks, known_predicates, all_task_objs, - vlm) + if CFG.vlm_predicate_vision_api_generate_ground_atoms: + generate_func = _generate_ground_atoms_with_vlm_oo_code_gen + else: + generate_func = _generate_ground_atoms_with_vlm_pure_visual_preds + ground_atoms_trajs = generate_func(img_option_trajs, env, train_tasks, + known_predicates, all_task_objs, vlm) return Dataset(option_segmented_trajs, ground_atoms_trajs) @@ -989,7 +1161,7 @@ def create_ground_atom_data_from_saved_img_trajs( # atoms that might be relevant to decision-making. if vlm is None: vlm = utils.create_vlm_by_name(CFG.vlm_model_name) # pragma: no cover - ground_atoms_trajs = _query_vlm_to_generate_ground_atoms_trajs( + ground_atoms_trajs = _generate_ground_atoms_with_vlm_pure_visual_preds( image_option_trajs, env, train_tasks, known_predicates, all_task_objs, vlm) # Finally, we just need to construct LowLevelTrajectories that we can diff --git a/predicators/datasets/vlm_input_data_prompts/vision_api/api_oo_state.txt b/predicators/datasets/vlm_input_data_prompts/vision_api/api_oo_state.txt new file mode 100644 index 0000000000..811308af27 --- /dev/null +++ b/predicators/datasets/vlm_input_data_prompts/vision_api/api_oo_state.txt @@ -0,0 +1,138 @@ +class State: + """A class representing the low-level state of the world. + + Attributes: + ----------- + data : Dict[Object, Array] + A dictionary mapping objects to their feature vectors. The feature vectors are numpy arrays. + + simulator_state : Optional[Any] + Some environments may need to store additional simulator state. This field is provided for that purpose. It is optional and defaults to None. + + Methods: + -------- + get(self, obj: Object, feature_name: str) -> Any: + This method looks up an object feature by name. It returns the value of + the feature. + + get_objects(self, object_type: Type) -> List[Object]: + This method returns objects of the given type in the order of + __iter__(). + """ + data: Dict[Object, Array] + # Some environments will need to store additional simulator state, so + # this field is provided. + simulator_state: Optional[Any] = None + + def get(self, obj: Object, feature_name: str) -> Any: + """Look up an object feature by name. + + Parameters: + ----------- + obj : Object + The object whose feature value is to be retrieved. + feature_name : str + The name of the feature to be retrieved. + + Returns: + -------- + Any + The value of the specified feature for the given object. + + Raises: + ------- + ValueError + If the specified feature name is not found in the object's type feature names. + + Examples: + --------- + >>> # An example for predicate Covers + >>> _block_type = Type("block", ["is_block", "is_target", "width", + "pose", "grasp"]) + >>> _target_type = Type("target", ["is_block", "is_target", "width", + "pose"]) + >>> block1 = Object("block1", _block_type) + >>> target1 = Object("target1", _target_type) + >>> state = State({ + block1: np.array([1.0, 0.0, 0.1, 0.2, -1.0]), + target1: np.array([0.0, 1.0, 0.05, 0.4])}) + >>> def _Covers_holds(state: State, objects: Sequence[Object]) -> + bool: + >>> block, target = objects + >>> block_pose = state.get(block, "pose") + >>> block_width = state.get(block, "width") + >>> target_pose = state.get(target, "pose") + >>> target_width = state.get(target, "width") + >>> return (block_pose-block_width/2 <= \ + target_pose-target_width/2) and \ + (block_pose+block_width/2 >= \ + target_pose+target_width/2) and \ + state.get(block, "grasp") == -1 + >>> _Covers = Predicate("Covers", [_block_type, _target_type], + _Covers_holds) + + >>> # Another example for predicate On + >>> _block_type = Type("block", ["pose_x", "pose_y", "pose_z", + "held", "color_r", "color_g", "color_b"]) + >>> block1 = Object("block1", _block_type) + >>> block2 = Object("block2", _block_type) + >>> state = State({ + block1: np.array([1.0, 3.0, 0.2, 0.0, 1.0, 0.0, 0.0]), + block2: np.array([2.0, 3.0, 0.3, 0.0, 0.0, 1.0, 0.0])}) + >>> on_tol = 0.01 + >>> def _On_holds(self, state: State, objects: Sequence[Object]) ->\ + bool: + >>> block1, block2 = objects + >>> if state.get(block1, "held") >= self.held_tol or \ + >>> state.get(block2, "held") >= self.held_tol: + >>> return False + >>> x1 = state.get(block1, "pose_x") + >>> y1 = state.get(block1, "pose_y") + >>> z1 = state.get(block1, "pose_z") + >>> x2 = state.get(block2, "pose_x") + >>> y2 = state.get(block2, "pose_y") + >>> z2 = state.get(block2, "pose_z") + >>> return np.allclose([x1, y1, z1], + [x2, y2, z2 + self._block_size], + atol=on_tol) + >>> _On = Predicate("On", [_block_type, _block_type], + _On_holds) + """ + + def get_objects(self, object_type: Type) -> List[Object]: + """Return objects of the given type in the order of __iter__(). + + Parameters: + ----------- + object_type : Type + The type of the objects to be retrieved. + + Returns: + -------- + List[Object] + A list of objects of the specified type, in the order they are + iterated over in the state. + + Examples: + --------- + >>> _robot_type = Type("robot", + ["x", "y", "z", "tilt", "wrist", "fingers"]) + >>> _cup_type = Type("cup", + ["x", "y", "capacity_liquid", "target_liquid", "current_liquid"]) + >>> robot = Object("robby", _robot_type) + >>> cup1 = Object("cup1", _cup_type) + >>> cup2 = Object("cup2", _cup_type) + >>> state = State({ + robot: np.array([5.0, 5.0, 10.0, 0.0, 0.0, 0.4]), + cup1: np.array([3.0, 2.0, 1.0, 0.75, 0.0]), + cup2: np.array([5.0, 6.0, 1.5, 1.125, 0.0])}) + >>> def _NotAboveCup_holds(state: State, + >>> objects: Sequence[Object]) -> bool: + >>> robot, jug = objects + >>> for cup in state.get_objects(_cup_type): + >>> if _robot_jug_above_cup(state, cup): + >>> return False + >>> return True + >>> _NotAboveCup = Predicate("NotAboveCup", [_robot_type, _jug_type], + _NotAboveCup_holds) + """ \ No newline at end of file diff --git a/predicators/datasets/vlm_input_data_prompts/vision_api/api_sym_predicate.txt b/predicators/datasets/vlm_input_data_prompts/vision_api/api_sym_predicate.txt new file mode 100644 index 0000000000..644405f360 --- /dev/null +++ b/predicators/datasets/vlm_input_data_prompts/vision_api/api_sym_predicate.txt @@ -0,0 +1,26 @@ +class Predicate: + """A class representing a predicate (a lifted classifier over states) in + the context of AI task planning. The function takes a state and a sequence + of objects as input, and returns a boolean value indicating whether the + property holds for those objects in that state. + + Attributes: + ----------- + name : str + The name of the predicate. + + types : Sequence[Type] + The types of the objects that the predicate applies to. This sequence + should have the same length as the sequence of objects passed to the + classifier. + + _classifier : Callable[[State, Sequence[Object]], bool] + The classifier function for the predicate. This function takes a state + and a sequence of objects as input, and returns a boolean value. The + objects in the sequence should correspond one-to-one with the types in + the 'types' attribute. The classifier should return True if the + predicate holds for those objects in that state, and False otherwise. + """ + name: str + types: Sequence[Type] + _classifier: Callable[[State, Sequence[Object]], bool] diff --git a/predicators/datasets/vlm_input_data_prompts/vision_api/prompt.outline b/predicators/datasets/vlm_input_data_prompts/vision_api/prompt.outline new file mode 100644 index 0000000000..4d6ff651bb --- /dev/null +++ b/predicators/datasets/vlm_input_data_prompts/vision_api/prompt.outline @@ -0,0 +1,27 @@ +You are a robotic vision system whose job is to output a structured set of predicates useful for describing important concepts in the following demonstration of a task. You will be provided with a list of actions used during the task, as well as images of states before and after every action execution. +Generating predicates that change before and after each action. After this, generate any other predicates that perhaps do not change but are still important to describing the demonstration shown. For each predicate you generate, also generate some predicates that are synonyms and antonyms so that any predicate that is even tangentially relevant to the demonstrations is generated. + +I will describe the API you should use for writing predicates and the environment the robot is in. +# API for Defining Predicates +Class definitions for `Predicate` and `State` are: +[STRUCT_DEFINITION] + +# The Environment +The environment includes the following object-type variables: +[TYPES_IN_ENV] + +# The demo trajectory is +[DEMO_TRAJECTORY] + +In your response, propose predicates in paragraphs as follows. For each proposal: +- Identify which property from above does it correspond to. +- Define predicate in a python block as follows: +```python +def classifier(state: State, objects: Sequence[Object]) -> bool: + # Implement the boolean classifier function here + ... + +name: str = ... # Define the predicate name here +param_types: Sequence[Type] = ... # A list of object-type variables for the predicate, using the ones defined in the environment + = Predicate(name, param_types, _holds) +``` \ No newline at end of file diff --git a/predicators/envs/burger.py b/predicators/envs/burger.py index 36fd55342d..3b4e9ba06d 100644 --- a/predicators/envs/burger.py +++ b/predicators/envs/burger.py @@ -58,16 +58,13 @@ class BurgerEnv(BaseEnv): _object_type = Type("object", []) _item_type = Type("item", [], _object_type) _station_type = Type("station", [], _object_type) - _robot_type = Type("robot", ["row", "col", "z", "fingers", "dir"], _object_type) - _patty_type = Type("patty", ["row", "col", "z"], _item_type) _tomato_type = Type("tomato", ["row", "col", "z"], _item_type) _cheese_type = Type("cheese", ["row", "col", "z"], _item_type) _bottom_bun_type = Type("bottom_bun", ["row", "col", "z"], _item_type) _top_bun_type = Type("top_bun", ["row", "col", "z"], _item_type) - _grill_type = Type("grill", ["row", "col", "z"], _station_type) _cutting_board_type = Type("cutting_board", ["row", "col", "z"], _station_type) diff --git a/predicators/envs/cover.py b/predicators/envs/cover.py index 1e2cb58fcd..1ff8f6b74b 100644 --- a/predicators/envs/cover.py +++ b/predicators/envs/cover.py @@ -27,15 +27,15 @@ class CoverEnv(BaseEnv): workspace_x: ClassVar[float] = 1.35 workspace_z: ClassVar[float] = 0.65 + # Types + _block_type = Type("block", + ["is_block", "is_target", "width", "pose", "grasp"]) + _target_type = Type("target", ["is_block", "is_target", "width", "pose"]) + _robot_type = Type("robot", ["hand", "pose_x", "pose_z"]) + def __init__(self, use_gui: bool = True) -> None: super().__init__(use_gui) - # Types - self._block_type = Type( - "block", ["is_block", "is_target", "width", "pose", "grasp"]) - self._target_type = Type("target", - ["is_block", "is_target", "width", "pose"]) - self._robot_type = Type("robot", ["hand", "pose_x", "pose_z"]) # Predicates self._IsBlock = Predicate("IsBlock", [self._block_type], self._IsBlock_holds) diff --git a/predicators/envs/kitchen.py b/predicators/envs/kitchen.py index 6201c2e2f8..bab273fb34 100644 --- a/predicators/envs/kitchen.py +++ b/predicators/envs/kitchen.py @@ -49,6 +49,7 @@ class KitchenEnv(BaseEnv): """Kitchen environment wrapping dm_control Kitchen.""" + # Types object_type = Type("object", ["x", "y", "z"]) gripper_type = Type("gripper", ["x", "y", "z", "qw", "qx", "qy", "qz"], parent=object_type) diff --git a/predicators/settings.py b/predicators/settings.py index 95384beaf8..7e232e2070 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -689,6 +689,7 @@ class GlobalSettings: # filepath to be used if offline_data_method is set to # saved_vlm_img_demos_folder vlm_trajs_folder_name = "" + vlm_predicate_vision_api_generate_ground_atoms = False @classmethod def get_arg_specific_settings(cls, args: Dict[str, Any]) -> Dict[str, Any]: diff --git a/predicators/structs.py b/predicators/structs.py index d058feae98..cf93c4f733 100644 --- a/predicators/structs.py +++ b/predicators/structs.py @@ -203,6 +203,36 @@ def pretty_str(self) -> str: suffix = "\n" + "#" * ll + "\n" return prefix + "\n\n".join(table_strs) + suffix + def dict_str(self, indent: int = 0, object_features: bool = True) -> str: + """Return a dictionary representation of the state.""" + state_dict = {} + for obj in self: + obj_dict = {} + if obj.type.name == "robot" or object_features: + for attribute, value in zip(obj.type.feature_names, self[obj]): + if isinstance(value, (float, int, np.float32)): + value = round(float(value), 1) + obj_dict[attribute] = value + obj_name = obj.name + state_dict[f"{obj_name}:{obj.type.name}"] = obj_dict + + # Create a string of n_space spaces + spaces = " " * indent + + # Create a PrettyPrinter with a large width + dict_str = spaces + "{" + n_keys = len(state_dict.keys()) + for i, (key, value) in enumerate(state_dict.items()): + value_str = ', '.join(f"'{k}': {v}" for k, v in value.items()) + if i == 0: + dict_str += f"'{key}': {{{value_str}}},\n" + elif i == n_keys - 1: + dict_str += spaces + f" '{key}': {{{value_str}}}" + else: + dict_str += spaces + f" '{key}': {{{value_str}}},\n" + dict_str += "}" + return dict_str + DefaultState = State({}) @@ -467,11 +497,12 @@ def replace_goal_with_alt_goal(self) -> Task: class EnvironmentTask: """An initial observation and goal description. - Environments produce environment tasks and agents produce and solve tasks. + Environments produce environment tasks and agents produce and solve + tasks. In fully observed settings, the init_obs will be a State and the - goal_description will be a Set[GroundAtom]. For convenience, we can convert - an EnvironmentTask into a Task in those cases. + goal_description will be a Set[GroundAtom]. For convenience, we can + convert an EnvironmentTask into a Task in those cases. """ init_obs: Observation goal_description: GoalDescription diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py index 897a3f39a7..65d2bbe74c 100644 --- a/tests/datasets/test_datasets.py +++ b/tests/datasets/test_datasets.py @@ -38,7 +38,45 @@ def _sample_completions(self, completions = [] for _ in range(num_completions): # If the query is asking for atom proposals. - if "Please provide predicates" in prompt: + if CFG.vlm_predicate_vision_api_generate_ground_atoms: + completion = ( + "```python\n" + "def _Covers_holds(state: State, objects: Sequence[Object])" + " -> bool:\n" + " block, target = objects\n" + " block_pose = state.get(block, 'pose')\n" + " block_width = state.get(block, 'width')\n" + " target_pose = state.get(target, 'pose')\n" + " target_width = state.get(target, 'width')\n" + " return (block_pose-block_width/2 <= " + "target_pose-target_width/2) and " + "(block_pose+block_width/2 >= target_pose+target_width/2) " + "and " + "state.get(block, 'grasp') == -1\n" + "_Covers = Predicate('Covers', [_block_type, _target_type]," + " _Covers_holds)" + "```\n" + "" + "```python\n" + "# An example of predicate name not found\n" + "def _HandEmpty_holds(state: State, objects: " + " Sequence[Object]) -> bool:\n" + " ... \n" + "# The predicate is instantiated similar to above" + "..." + "```\n" + "" + "```python\n" + "# An example of attribute error\n" + "def _IsBlue_holds(state: State, objects: " + " Sequence[Object]) -> bool:\n" + " block, = objects\n" + " block_color = state.get(block, 'color')\n" + " return block_color == 'blue'\n" + "_IsBlue = Predicate('IsBlue', [_block_type]," + " _IsBlue_holds)" + "```\n") + elif "Please provide predicates" in prompt: completion = "*Holding(spoon)\n*Fizz(buzz)\n" + \ "*Submerged(teabag)\n*Submerged(spoon)\n*IsRobot(robby)" # Else, if the query is asking for particular values. @@ -748,9 +786,8 @@ def test_loading_txt_files(): ).name == "PickPlace" -def test_create_ground_atom_data_from_generated_demos(): - """Tests for the create_ground_atom_data_from_generated_demos method.""" - utils.reset_config({ +@pytest.mark.parametrize( + "config", [{ "env": "cover", "approach": "oracle", "offline_data_method": "demo", @@ -759,7 +796,20 @@ def test_create_ground_atom_data_from_generated_demos(): "num_train_tasks": 1, "included_options": "PickPlace", "excluded_predicates": "all", - }) + }, { + "env": "cover", + "approach": "oracle", + "offline_data_method": "demo", + "offline_data_planning_timeout": 500, + "option_learner": "no_learning", + "num_train_tasks": 1, + "included_options": "PickPlace", + "excluded_predicates": "all", + "vlm_predicate_vision_api_generate_ground_atoms": True + }]) +def test_create_ground_atom_data_from_generated_demos(config): + """Tests for the create_ground_atom_data_from_generated_demos method.""" + utils.reset_config(config) env = CoverEnv() train_tasks = [t.task for t in env.get_train_tasks()] predicates, _ = utils.parse_config_excluded_predicates(env) From 40fc50f102965f608834ff42dbab77238cfde37d Mon Sep 17 00:00:00 2001 From: Nishanth Kumar Date: Wed, 7 Aug 2024 09:49:13 -0700 Subject: [PATCH 36/38] VLM Open-Loop Approach (#1702) * init stuff * making progress * progress! * making progress! * progress! Still need to construct few-shot prompt, but basically there! * imgs are prepped; got to make actual prompt and query model now * ready to go! * everything except tests done! * works for envs with continuous params now! * woops - mypy * yapf * testing utils done * ashay comments --- .../approaches/llm_open_loop_approach.py | 64 +----- .../approaches/vlm_open_loop_approach.py | 202 ++++++++++++++++++ .../vlm_planning_prompts/few_shot.txt | 38 ++++ .../vlm_planning_prompts/no_few_shot.txt | 28 +++ predicators/settings.py | 5 + predicators/structs.py | 10 + predicators/utils.py | 158 ++++++++++++-- tests/test_utils.py | 72 ++++++- 8 files changed, 505 insertions(+), 72 deletions(-) create mode 100644 predicators/approaches/vlm_open_loop_approach.py create mode 100644 predicators/approaches/vlm_planning_prompts/few_shot.txt create mode 100644 predicators/approaches/vlm_planning_prompts/no_few_shot.txt diff --git a/predicators/approaches/llm_open_loop_approach.py b/predicators/approaches/llm_open_loop_approach.py index d16b4cdcb1..7ced989beb 100644 --- a/predicators/approaches/llm_open_loop_approach.py +++ b/predicators/approaches/llm_open_loop_approach.py @@ -40,10 +40,10 @@ """ from __future__ import annotations -import logging from typing import Collection, Dict, Iterator, List, Optional, Sequence, Set, \ Tuple +from predicators import utils from predicators.approaches import ApproachFailure from predicators.approaches.nsrt_metacontroller_approach import \ NSRTMetacontrollerApproach @@ -135,63 +135,11 @@ def _llm_prediction_to_option_plan( ParameterizedOptions coupled with a list of objects that will be used to ground the ParameterizedOption.""" option_plan: List[Tuple[ParameterizedOption, Sequence[Object]]] = [] - # Setup dictionaries enabling us to easily map names to specific - # Python objects during parsing. - option_name_to_option = {op.name: op for op in self._initial_options} - obj_name_to_obj = {o.name: o for o in objects} - # We assume the LLM's output is such that each line contains a - # option_name(obj0:type0, obj1:type1,...). - options_str_list = llm_prediction.split('\n') - for option_str in options_str_list: - option_str_stripped = option_str.strip() - option_name = option_str_stripped.split('(')[0] - # Skip empty option strs. - if not option_str: - continue - if option_name not in option_name_to_option.keys() or \ - "(" not in option_str: - logging.info( - f"Line {option_str} output by LLM doesn't " - "contain a valid option name. Terminating option plan " - "parsing.") - break - option = option_name_to_option[option_name] - # Now that we have the option, we need to parse out the objects - # along with specified types. - typed_objects_str_list = option_str_stripped.split('(')[1].strip( - ')').split(',') - objs_list = [] - malformed = False - for i, type_object_string in enumerate(typed_objects_str_list): - object_type_str_list = type_object_string.strip().split(':') - # We expect this list to be [object_name, type_name]. - if len(object_type_str_list) != 2: - logging.info(f"Line {option_str} output by LLM has a " - "malformed object-type list.") - malformed = True - break - object_name = object_type_str_list[0] - type_name = object_type_str_list[1] - if object_name not in obj_name_to_obj.keys(): - logging.info(f"Line {option_str} output by LLM has an " - "invalid object name.") - malformed = True - break - obj = obj_name_to_obj[object_name] - # Check that the type of this object agrees - # with what's expected given the ParameterizedOption. - if type_name != option.types[i].name: - logging.info(f"Line {option_str} output by LLM has an " - "invalid type name.") - malformed = True - break - objs_list.append(obj) - # The types of the objects match, but we haven't yet checked if - # all arguments of the option have an associated object. - if len(objs_list) != len(option.types): - malformed = True - if not malformed: - option_plan.append((option, objs_list)) + option_plan_with_cont_params = utils.\ + parse_model_output_into_option_plan( + llm_prediction, objects, self._types, self._initial_options, False) + option_plan = [(option, objs) + for option, objs, _ in option_plan_with_cont_params] return option_plan def learn_from_offline_dataset(self, dataset: Dataset) -> None: diff --git a/predicators/approaches/vlm_open_loop_approach.py b/predicators/approaches/vlm_open_loop_approach.py new file mode 100644 index 0000000000..0bf9d23bb3 --- /dev/null +++ b/predicators/approaches/vlm_open_loop_approach.py @@ -0,0 +1,202 @@ +"""Open-loop vision-language model (VLM) planner approach based on the 'Look +Before You Leap' paper (https://arxiv.org/abs/2311.17842). Depending on command +line options (specifically --vlm_open_loop_use_training_demos), the VLM can be +made to use the training trajectories as few-shot examples for solving the +task, or directly solve the task with no few-shot prompting. + +Example command in burger that doesn't use few-shot examples: +python predicators/main.py --env burger --approach vlm_open_loop --seed 0 \ +--num_train_tasks 0 --num_test_tasks 1 --bilevel_plan_without_sim True \ +--make_failure_videos --sesame_task_planner fdopt --vlm_model_name gpt-4o + +Example command that does have few-shot examples: +python predicators/main.py --env burger --approach vlm_open_loop --seed 0 \ +--num_train_tasks 1 --num_test_tasks 1 --bilevel_plan_without_sim True \ +--make_failure_videos --sesame_task_planner fdopt --debug \ +--vlm_model_name gemini-1.5-pro-latest --vlm_open_loop_use_training_demos True +""" + +from __future__ import annotations + +from typing import Callable, List, Sequence, Set + +import numpy as np +import PIL +from PIL import ImageDraw + +from predicators import utils +from predicators.approaches import ApproachFailure +from predicators.approaches.bilevel_planning_approach import \ + BilevelPlanningApproach +from predicators.settings import CFG +from predicators.structs import Action, Box, Dataset, ParameterizedOption, \ + Predicate, State, Task, Type, _Option + + +class VLMOpenLoopApproach(BilevelPlanningApproach): # pragma: no cover + """VLMOpenLoopApproach definition. + + NOTE: we don't test this approach because it requires an environment + that has rendering, and our current envs that have this are burger + and kitchen, which are slow/untestable on GitHub remote... + """ + + def __init__(self, initial_predicates: Set[Predicate], + initial_options: Set[ParameterizedOption], types: Set[Type], + action_space: Box, train_tasks: List[Task]) -> None: + super().__init__(initial_predicates, initial_options, types, + action_space, train_tasks) + # Set up the vlm and base prompt. + self._vlm = utils.create_vlm_by_name(CFG.vlm_model_name) + filepath_to_vlm_prompt = utils.get_path_to_predicators_root() + \ + "/predicators/approaches/vlm_planning_prompts/no_few_shot.txt" + if CFG.vlm_open_loop_use_training_demos: + filepath_to_vlm_prompt = utils.get_path_to_predicators_root() + \ + "/predicators/approaches/vlm_planning_prompts/few_shot.txt" + with open(filepath_to_vlm_prompt, "r", encoding="utf-8") as f: + self.base_prompt = f.read() + self._prompt_state_imgs_list: List[PIL.Image.Image] = [] + self._prompt_demos_str = "" + + @classmethod + def get_name(cls) -> str: + return "vlm_open_loop" + + @property + def is_learning_based(self) -> bool: + return True + + def learn_from_offline_dataset(self, dataset: Dataset) -> None: + """Adds the images and plans from the training dataset to the base + prompt for use at test time!""" + if not CFG.vlm_open_loop_use_training_demos: + return None + # Crawl thru the dataset and pull out all the images. + # For each image, add text to it in the bototm left indicating the + # trajectory and timestep it's from. + assert dataset.trajectories[0].states[0].simulator_state is not None + assert isinstance( + dataset.trajectories[0].states[0].simulator_state["images"], List) + num_imgs_per_state = len( + dataset.trajectories[0].states[0].simulator_state["images"]) + self._prompt_demos_str = "" + for traj_num, traj in enumerate(dataset.trajectories): + traj_goal = self._train_tasks[traj.train_task_idx].goal + self._prompt_demos_str += f"Demonstration {traj_num}, " + \ + f"Goal: {str(sorted(traj_goal))}\n" + for state_num, state in enumerate(traj.states): + assert state.simulator_state is not None + assert len( + state.simulator_state["images"]) == num_imgs_per_state + for img_num, img in enumerate(state.simulator_state["images"]): + pil_img = PIL.Image.fromarray(img) # type: ignore + width, height = pil_img.size + font_size = 15 + text = f"Demonstration {traj_num}, " + \ + f"State {state_num}, Image {img_num}" + draw = ImageDraw.Draw(pil_img) + font = utils.get_scaled_default_font(draw, font_size) + text_width, text_height = draw.textbbox((0, 0), + text, + font=font)[2:] + # Create a new image with additional space for text! + new_image = PIL.Image.new( + "RGB", (width, height + text_height + 10), "white") + new_image.paste(pil_img, (0, 0)) + draw = ImageDraw.Draw(new_image) + text_x = (width - text_width) / 2 + text_y = height + 5 + draw.text((text_x, text_y), text, font=font, fill="black") + self._prompt_state_imgs_list.append(draw._image) # pylint:disable=protected-access + for action_num, action in enumerate(traj.actions): + self._prompt_demos_str += f"Action {action_num}, from " + \ + f"state {action_num} is {action.get_option()}\n" + return None + + def _get_current_nsrts(self) -> Set[utils.NSRT]: + """This method doesn't explicitly learn NSRTs, so we simply return the + empty set.""" + return set() + + def _solve(self, task: Task, timeout: int) -> Callable[[State], Action]: + try: + option_plan = self._query_vlm_for_option_plan(task) + except Exception as e: + raise ApproachFailure( + f"VLM failed to produce coherent option plan. Reason: {e}") + + policy = utils.option_plan_to_policy(option_plan) + + def _policy(s: State) -> Action: + try: + return policy(s) + except utils.OptionExecutionFailure as e: + raise ApproachFailure(e.args[0], e.info) + + return _policy + + def _query_vlm_for_option_plan(self, task: Task) -> Sequence[_Option]: + init_state = task.init + assert init_state.simulator_state is not None + assert isinstance(init_state.simulator_state["images"], List) + curr_options = sorted(self._initial_options) + imgs = init_state.simulator_state["images"] + pil_imgs = [ + PIL.Image.fromarray(img_arr) # type: ignore + for img_arr in imgs + ] + imgs_for_vlm = [] + for img_num, pil_img in enumerate(pil_imgs): + draw = ImageDraw.Draw(pil_img) + img_font = utils.get_scaled_default_font(draw, 10) + img_with_txt = utils.add_text_to_draw_img( + draw, (50, 50), f"Initial state to plan from, Image {img_num}", + img_font) + imgs_for_vlm.append(img_with_txt._image) # pylint:disable=protected-access + options_str = "\n".join( + str(opt) + ", params_space=" + str(opt.params_space) + for opt in curr_options) + objects_list = sorted(set(task.init)) + objects_str = "\n".join(str(obj) for obj in objects_list) + goal_expr_list = sorted(set(task.goal)) + type_hierarchy_str = utils.create_pddl_types_str(self._types) + goal_str = "\n".join(str(obj) for obj in goal_expr_list) + if not CFG.vlm_open_loop_use_training_demos: + prompt = self.base_prompt.format(options=options_str, + typed_objects=objects_str, + type_hierarchy=type_hierarchy_str, + goal_str=goal_str) + vlm_output = self._vlm.sample_completions( + prompt, + imgs_for_vlm, + temperature=CFG.vlm_temperature, + seed=CFG.seed, + num_completions=1) + else: + prompt = self.base_prompt.format( + options=options_str, + demonstration_trajs=self._prompt_demos_str, + typed_objects=objects_str, + type_hierarchy=type_hierarchy_str, + goal_str=goal_str) + vlm_output = self._vlm.sample_completions( + prompt, + self._prompt_state_imgs_list + imgs_for_vlm, + temperature=CFG.vlm_temperature, + seed=CFG.seed, + num_completions=1) + plan_prediction_txt = vlm_output[0] + option_plan: List[_Option] = [] + try: + start_index = plan_prediction_txt.index("Plan:\n") + len("Plan:\n") + parsable_plan_prediction = plan_prediction_txt[start_index:] + except ValueError: + raise ValueError("VLM output is badly formatted; cannot " + "parse plan!") + parsed_option_plan = utils.parse_model_output_into_option_plan( + parsable_plan_prediction, objects_list, self._types, + self._initial_options, True) + for option_tuple in parsed_option_plan: + option_plan.append(option_tuple[0].ground( + option_tuple[1], np.array(option_tuple[2]))) + return option_plan diff --git a/predicators/approaches/vlm_planning_prompts/few_shot.txt b/predicators/approaches/vlm_planning_prompts/few_shot.txt new file mode 100644 index 0000000000..002ed0ec14 --- /dev/null +++ b/predicators/approaches/vlm_planning_prompts/few_shot.txt @@ -0,0 +1,38 @@ +You are highly skilled in robotic task planning, breaking down intricate and long-term tasks into distinct primitive actions. +Consider the following skills a robotic agent can perform. Note that each of these skills takes the form of a `ParameterizedOption` and may have both discrete arguments (indicated by the `types` field, referring to objects of particular types), +as well as continuous arguments (indicated by `params_space` field, which is formatted as `Box([, , ...], [, , ...], (,), )`). + +{options} + + +Here are a number of example demonstrations of solving a range of goals from an expert demonstrator. For each of these examples, you are provided +with the goal the demonstrator was trying to achieve, the plan (i.e., sequence of skills with discrete and continuous arguments specified), as well +as images corresponding to the state of the environment at every timestep (starting at 0, when the 0th skill is executed). Note that the state images +are annotated with text indicating which demonstration and timestep they belong to. Additionally, note that a state might have more than one image +associated with it. + +{demonstration_trajs} + + +To solve the task presented below, you are only allowed to use the provided skills. It's essential to stick to the format of these basic skills. When creating a plan, replace +the arguments of each skill with specific items or continuous parameters. You can first describe the provided scene and what it indicates about the provided +task objects to help you come up with a plan. + +Here is a list of objects present in this scene for this task, along with their type (formatted as : ): +{typed_objects} + +And here are the available types (formatted in PDDL style as ` ... - `). You can infer a hierarchy of types via this: +{type_hierarchy} + +Finally, here is an expression corresponding to the current task goal that must be achieved: +{goal_str} + +Please return a plan that achieves the provided goal from an initial state depicted by the image(s) below. +Please provide your output in the following format (excluding the angle brackets and ellipsis, which are just for illustration purposes). +Be sure to include the parens '(' and ')', as well as square brackets '[' and ']' even if there are no objects/continuous parameters. +Do not bold or italicize or otherwise apply any extra formaating to the plan text: + +Plan: +(:, :, ...)[, , ...] +(:, :, ...)[, , ...] +... diff --git a/predicators/approaches/vlm_planning_prompts/no_few_shot.txt b/predicators/approaches/vlm_planning_prompts/no_few_shot.txt new file mode 100644 index 0000000000..da7fbc66c6 --- /dev/null +++ b/predicators/approaches/vlm_planning_prompts/no_few_shot.txt @@ -0,0 +1,28 @@ +You are highly skilled in robotic task planning, breaking down intricate and long-term tasks into distinct primitive actions. +Consider the following skills a robotic agent can perform. Note that each of these skills takes the form of a `ParameterizedOption` and may have both discrete arguments (indicated by the `types` field, referring to objects of particular types), +as well as continuous arguments (indicated by `params_space` field, which is formatted as `Box([, , ...], [, , ...], (,), )`). + +{options} + +You are only allowed to use the provided skills. It's essential to stick to the format of these basic skills. When creating a plan, replace +the arguments of each skill with specific items or continuous parameters. You can first describe the provided scene and what it indicates about the provided +task objects to help you come up with a plan. + +Here is a list of objects present in this scene for this task, along with their type (formatted as : ): +{typed_objects} + +And here are the available types (formatted in PDDL style as ` ... - `). You can infer a hierarchy of types via this: +{type_hierarchy} + +Finally, here is an expression corresponding to the current task goal that must be achieved: +{goal_str} + +Please return a plan that achieves the provided goal from an initial state depicted by the image(s) below. +Please provide your output in the following format (excluding the angle brackets and ellipsis, which are just for illustration purposes). +Be sure to include the parens '(' and ')', as well as square brackets '[' and ']' even if there are no objects/continuous parameters. +Do not bold or italicize or otherwise apply any extra formaating to the plan text: + +Plan: +(:, :, ...)[, , ...] +(:, :, ...)[, , ...] +... diff --git a/predicators/settings.py b/predicators/settings.py index 7e232e2070..c3e9dfe57b 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -418,6 +418,11 @@ class GlobalSettings: # parameters for vision language models # gemini-1.5-pro-latest, gpt-4-turbo, gpt-4o vlm_model_name = "gemini-pro-vision" + vlm_temperature = 0.0 + vlm_num_completions = 1 + + # parameters for the vlm_open_loop planning approach + vlm_open_loop_use_training_demos = False # SeSamE parameters sesame_task_planner = "astar" # "astar" or "fdopt" or "fdsat" diff --git a/predicators/structs.py b/predicators/structs.py index cf93c4f733..a27c20947b 100644 --- a/predicators/structs.py +++ b/predicators/structs.py @@ -31,6 +31,16 @@ def dim(self) -> int: """Dimensionality of the feature vector of this object type.""" return len(self.feature_names) + def get_ancestors(self) -> Set[Type]: + """Get the set of all types that are ancestors (i.e. parents, + grandparents, great-grandparents, etc.) of the current type.""" + curr_type: Optional[Type] = self + ancestors_set = set() + while curr_type is not None: + ancestors_set.add(curr_type) + curr_type = curr_type.parent + return ancestors_set + def __call__(self, name: str) -> _TypedEntity: """Convenience method for generating _TypedEntities.""" if name.startswith("?"): diff --git a/predicators/utils.py b/predicators/utils.py index f2034dbca1..3500562b56 100644 --- a/predicators/utils.py +++ b/predicators/utils.py @@ -2227,6 +2227,14 @@ def _stripped_classifier( return VLMPredicate(name, types, _stripped_classifier, get_vlm_query_str) +def create_llm_by_name( + model_name: str) -> LargeLanguageModel: # pragma: no cover + """Create particular llm using a provided name.""" + if "gemini" in model_name: + return GoogleGeminiLLM(model_name) + return OpenAILLM(model_name) + + def create_vlm_by_name( model_name: str) -> VisionLanguageModel: # pragma: no cover """Create particular vlm using a provided name.""" @@ -2235,12 +2243,132 @@ def create_vlm_by_name( return OpenAIVLM(model_name) -def create_llm_by_name( - model_name: str) -> LargeLanguageModel: # pragma: no cover - """Create particular llm using a provided name.""" - if "gemini" in model_name: - return GoogleGeminiLLM(model_name) - return OpenAILLM(model_name) +def parse_model_output_into_option_plan( + model_prediction: str, objects: Collection[Object], + types: Collection[Type], options: Collection[ParameterizedOption], + parse_continuous_params: bool +) -> List[Tuple[ParameterizedOption, Sequence[Object], Sequence[float]]]: + """Assuming text for an option plan that is predicted as text by a large + model, parse it into a sequence of ParameterizedOptions coupled with a list + of objects and continuous parameters that will be used to ground the + ParameterizedOption. + + We assume the model's output is such that each line is formatted as + option_name(obj0:type0, obj1:type1,...)[continuous_param0, + continuous_param1, ...]. + """ + option_plan: List[Tuple[ParameterizedOption, Sequence[Object], + Sequence[float]]] = [] + # Setup dictionaries enabling us to easily map names to specific + # Python objects during parsing. + option_name_to_option = {op.name: op for op in options} + type_name_to_type = {typ.name: typ for typ in types} + obj_name_to_obj = {o.name: o for o in objects} + options_str_list = model_prediction.split('\n') + for option_str in options_str_list: + option_str_stripped = option_str.strip() + option_name = option_str_stripped.split('(')[0] + # Skip empty option strs. + if not option_str: + continue + if option_name not in option_name_to_option.keys() or \ + "(" not in option_str: + logging.info( + f"Line {option_str} output by model doesn't " + "contain a valid option name. Terminating option plan " + "parsing.") + break + if parse_continuous_params and "[" not in option_str: + logging.info( + f"Line {option_str} output by model doesn't contain a " + "'[' and is thus improperly formatted.") + break + option = option_name_to_option[option_name] + # Now that we have the option, we need to parse out the objects + # along with specified types. + try: + start_index = option_str_stripped.index('(') + 1 + end_index = option_str_stripped.index(')', start_index) + except ValueError: + logging.info( + f"Line {option_str} output by model is improperly formatted.") + break + typed_objects_str_list = option_str_stripped[ + start_index:end_index].split(',') + objs_list = [] + continuous_params_list = [] + malformed = False + for i, type_object_string in enumerate(typed_objects_str_list): + object_type_str_list = type_object_string.strip().split(':') + # We expect this list to be [object_name, type_name]. + if len(object_type_str_list) != 2: + logging.info(f"Line {option_str} output by model has a " + "malformed object-type list.") + malformed = True + break + object_name = object_type_str_list[0] + type_name = object_type_str_list[1] + if object_name not in obj_name_to_obj.keys(): + logging.info(f"Line {option_str} output by model has an " + "invalid object name.") + malformed = True + break + obj = obj_name_to_obj[object_name] + # Check that the type of this object agrees + # with what's expected given the ParameterizedOption. + if type_name not in type_name_to_type: + logging.info(f"Line {option_str} output by model has an " + "invalid type name.") + malformed = True + break + try: + if option.types[i] not in type_name_to_type[ + type_name].get_ancestors(): + logging.info( + f"Line {option_str} output by model has an " + "invalid type that doesn't agree with the option" + f"{option}") + malformed = True + break + except IndexError: + # In this case, there's more supplied arguments than the + # option has. + logging.info(f"Line {option_str} output by model has an " + "too many object arguments for option" + f"{option}") + malformed = True + break + objs_list.append(obj) + # The types of the objects match, but we haven't yet checked if + # all arguments of the option have an associated object. + if len(objs_list) != len(option.types): + malformed = True + # Now, we attempt to parse out the continuous parameters. + if parse_continuous_params: + params_str_list = option_str_stripped.split('[')[1].strip( + ']').split(',') + for i, continuous_params_str in enumerate(params_str_list): + stripped_continuous_param_str = continuous_params_str.strip() + if len(stripped_continuous_param_str) == 0: + continue + try: + curr_cont_param = float(stripped_continuous_param_str) + except ValueError: + logging.info(f"Line {option_str} output by model has an " + "invalid continouous parameter that can't be" + "converted to a float.") + malformed = True + break + continuous_params_list.append(curr_cont_param) + if len(continuous_params_list) != option.params_space.shape[0]: + logging.info(f"Line {option_str} output by model has " + "invalid continouous parameter(s) that don't " + f"agree with {option}{option.params_space}.") + malformed = True + break + if not malformed: + option_plan.append((option, objs_list, continuous_params_list)) + return option_plan def query_vlm_for_atom_vals( @@ -3062,12 +3190,8 @@ def _atoms_to_pyperplan_facts( ############################## End Pyperplan Glue ############################## -def create_pddl_domain(operators: Collection[NSRTOrSTRIPSOperator], - predicates: Collection[Predicate], - types: Collection[Type], domain_name: str) -> str: - """Create a PDDL domain str from STRIPSOperators or NSRTs.""" - # Sort everything to ensure determinism. - preds_lst = sorted(predicates) +def create_pddl_types_str(types: Collection[Type]) -> str: + """Create a PDDL-style types string that handles hierarchy correctly.""" # Case 1: no type hierarchy. if all(t.parent is None for t in types): types_str = " ".join(t.name for t in sorted(types)) @@ -3094,6 +3218,16 @@ def create_pddl_domain(operators: Collection[NSRTOrSTRIPSOperator], else: child_type_str = " ".join(t.name for t in child_types) types_str += f"\n {child_type_str} - {parent_type.name}" + return types_str + + +def create_pddl_domain(operators: Collection[NSRTOrSTRIPSOperator], + predicates: Collection[Predicate], + types: Collection[Type], domain_name: str) -> str: + """Create a PDDL domain str from STRIPSOperators or NSRTs.""" + # Sort everything to ensure determinism. + preds_lst = sorted(predicates) + types_str = create_pddl_types_str(types) ops_lst = sorted(operators) preds_str = "\n ".join(pred.pddl_str() for pred in preds_lst) ops_strs = "\n\n ".join(op.pddl_str() for op in ops_lst) diff --git a/tests/test_utils.py b/tests/test_utils.py index e951f070a8..f5dd4743b8 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -20,8 +20,8 @@ from predicators.pretrained_model_interface import VisionLanguageModel from predicators.settings import CFG from predicators.structs import NSRT, Action, DefaultState, DummyOption, \ - GroundAtom, LowLevelTrajectory, ParameterizedOption, Predicate, Segment, \ - State, STRIPSOperator, Type, Variable, VLMPredicate + GroundAtom, LowLevelTrajectory, Object, ParameterizedOption, Predicate, \ + Segment, State, STRIPSOperator, Type, Variable, VLMPredicate from predicators.utils import GoalCountHeuristic, _PyperplanHeuristicWrapper, \ _TaskPlanningHeuristic @@ -3457,3 +3457,71 @@ def test_oracle_feature_selection(): utils.construct_active_sampler_input(state, [robot, cup], params, NavigateToCup) assert "Oracle feature selection" in str(e) + + +def test_parse_model_output_into_option_plan(): + """Test the utility function that turns a prediction by a large model into + a structure from which an option plan can be made.""" + # NOTE: all these tests are for the failure cases of this function, since + # the success cases are covered when we test the LLMOpenLoopApproach + utils.reset_config() + options = get_gt_options("cover") + options_str = "wakanda\n" + assert len( + utils.parse_model_output_into_option_plan(options_str, [], [], options, + False)) == 0 + options_str = "PickPlace()\n" + assert len( + utils.parse_model_output_into_option_plan(options_str, [], [], options, + True)) == 0 + options_str = "PickPlace(\n" + assert len( + utils.parse_model_output_into_option_plan(options_str, [], [], options, + False)) == 0 + options_str = "PickPlace(forever)\n" + assert len( + utils.parse_model_output_into_option_plan(options_str, [], [], options, + False)) == 0 + obj_type = Type("object_type", []) + obj = Object("obj", obj_type) + options_str = "PickPlace(canard:object_type)\n" + assert len( + utils.parse_model_output_into_option_plan(options_str, [obj], + [obj_type], options, + False)) == 0 + options_str = "PickPlace(obj:canard_type)\n" + assert len( + utils.parse_model_output_into_option_plan(options_str, [obj], + [obj_type], options, + False)) == 0 + options_str = "PickPlace(obj:object_type)\n" + assert len( + utils.parse_model_output_into_option_plan(options_str, [obj], + [obj_type], options, + False)) == 0 + options_str = "PickPlace()[ ,]" + assert len( + utils.parse_model_output_into_option_plan(options_str, [obj], + [obj_type], options, + True)) == 0 + options_str = "PickPlace()[not_a_param]" + assert len( + utils.parse_model_output_into_option_plan(options_str, [obj], + [obj_type], options, + True)) == 0 + options_str = "PickPlace()[0.5, 0.5]" + assert len( + utils.parse_model_output_into_option_plan(options_str, [obj], + [obj_type], options, + True)) == 0 + options = get_gt_options("blocks") + options_str = "Pick(obj:object_type)" + assert len( + utils.parse_model_output_into_option_plan(options_str, [obj], + [obj_type], options, + False)) == 0 + options_str = "Pick()" + assert len( + utils.parse_model_output_into_option_plan(options_str, [obj], + [obj_type], options, + False)) == 0 From 1d2cce664c720690cf0399ba5972365ffe907734 Mon Sep 17 00:00:00 2001 From: Ashay Athalye Date: Thu, 22 Aug 2024 13:57:54 -0400 Subject: [PATCH 37/38] Create BurgerEnv variant without a dedicated move option/operator (#1706) * Initial commit. * Add first-draft of support for cropped images that focus on relevant objects. * Update approach to pass in full and cropped before/after images during labeling. * Update prompt and add some debug statements. * Add option to compute preconditions via soft intersection in cluter and intersect. * Investigate extra operators from label errors that cause us to fail on test tasks. * Start adding some support for goal variation in BurgerEnv. * Add a GoalHack for the third goal in BurgerEnv and fix set of marks to not extend past the edge of the grid. * Add debugging code via a manually specified plan. * Initial commit. * Update nsrts and options for burger_no_move. * Fix burger_no_move nsrts and options so that basic goals are achieved by oracle. * Get one basic generalization result in burger to work. * Edit goals towards a different generalization result. * Progress. * Achieved next generalization result. * Fix VLM atom proposal to include all groundings of each proposed predicate. * Update get_tasks(). * Get 2/3 categories of generalization to work. * Get all three kinds of tasks to work. * Refactor goalhack predicates and ensure BurgerEnv still works. * Fix some formatting issues. * Fix more linting issues. * Get formatting, linting, type-checking tests to pass. * Get mypy tests to pass. * Run autoformatter. * Start to address comments. * Get some tests to pass. * Add an option to skip hill-climbing and select all debug predicates directly. * Run autoformatter. * Progress towards getting burger_no_move to work after changes. * Update debug VLM atom proposal to use existing code that was there to handle that case. * Fix handling of all groundings of VLM atom proposals to avoid labeling them when their objects are not the trajectory being considered. * Remove comment. * Run autoformatter. * Fix typo in comment. * Replace original atom labeling file. * Remove space. * Add unit tests for grammar search debug predicates and soft intersection in cluster and intersect. * Fix burger text. * Include cropped image into prompt only when flag is set. * Add test for including cropped images in ground atom data generation. * Run autoformatter. * Add more tests for include cropped images. * Add test for ImageOptionTrajectory. * Update tests for BurgerEnv. * Write tests for BurgerNoMoveEnv. * Update burger no move test. * Update burger no move tests. * Finish burger no move tests. * Fix broken tests. --- .../grammar_search_invention_approach.py | 55 +- .../approaches/vlm_open_loop_approach.py | 10 +- .../datasets/generate_atom_trajs_with_vlm.py | 152 +++- .../img_option_diffs_burger.txt | 3 + .../img_option_diffs_label_history_crops.txt | 3 + predicators/envs/assets/imgs/cut_lettuce.png | Bin 0 -> 391413 bytes .../assets/imgs/realistic_cooked_patty.png | Bin 0 -> 32492 bytes .../imgs/realistic_cooked_patty_full.png | Bin 0 -> 211345 bytes .../envs/assets/imgs/realistic_raw_patty.png | Bin 0 -> 33199 bytes .../assets/imgs/realistic_raw_patty_full.png | Bin 0 -> 208108 bytes .../envs/assets/imgs/uncut_lettuce.png | Bin 0 -> 264973 bytes predicators/envs/base_env.py | 8 +- predicators/envs/burger.py | 711 ++++++++++++++++-- predicators/envs/vlm_envs.py | 6 +- .../ground_truth_models/burger/__init__.py | 12 +- .../ground_truth_models/burger/nsrts.py | 169 ++++- .../ground_truth_models/burger/options.py | 307 ++++++-- .../strips_learning/base_strips_learner.py | 41 + .../strips_learning/clustering_learner.py | 7 +- predicators/settings.py | 13 +- predicators/structs.py | 6 + .../test_grammar_search_invention_approach.py | 49 +- tests/datasets/test_datasets.py | 57 ++ tests/envs/test_burger.py | 187 ++++- tests/envs/test_cover.py | 1 + .../test_clustering_learner.py | 45 ++ tests/test_structs.py | 36 +- 27 files changed, 1702 insertions(+), 176 deletions(-) create mode 100644 predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_burger.txt create mode 100644 predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history_crops.txt create mode 100644 predicators/envs/assets/imgs/cut_lettuce.png create mode 100644 predicators/envs/assets/imgs/realistic_cooked_patty.png create mode 100644 predicators/envs/assets/imgs/realistic_cooked_patty_full.png create mode 100644 predicators/envs/assets/imgs/realistic_raw_patty.png create mode 100644 predicators/envs/assets/imgs/realistic_raw_patty_full.png create mode 100644 predicators/envs/assets/imgs/uncut_lettuce.png diff --git a/predicators/approaches/grammar_search_invention_approach.py b/predicators/approaches/grammar_search_invention_approach.py index 001ecc0d84..8195ce5536 100644 --- a/predicators/approaches/grammar_search_invention_approach.py +++ b/predicators/approaches/grammar_search_invention_approach.py @@ -6,6 +6,7 @@ import abc import itertools import logging +from collections import defaultdict from dataclasses import dataclass, field from functools import cached_property from operator import le @@ -403,7 +404,7 @@ def enumerate(self) -> Iterator[Tuple[Predicate, float]]: raise NotImplementedError("Override me!") -_DEBUG_PREDICATE_PREFIXES = { +_DEBUG_GEOMETRIC_PREDICATES = { "tools": [ "NOT-((0:robot).fingers<=[idx 0]0.5)", # HandEmpty "NOT-((0:screw).is_held<=[idx 0]0.5)", # HoldingScrew @@ -463,24 +464,43 @@ def enumerate(self) -> Iterator[Tuple[Predicate, float]]: "((0:button).y<=[idx 0]3.01)", # ButtonReachableByRobot "NOT-((0:button).y<=[idx 0]3.01)", # ButtonNotReachableByRobot ], + "burger": [ + "((0:robot).fingers<=[idx 0]0.5)" + ], + "burger_no_move": [ + "((0:robot).fingers<=[idx 0]0.5)" + ], "unittest": [ "((0:robot).hand<=[idx 0]0.65)", "((0:block).grasp<=[idx 0]0.0)", "NOT-Forall[0:block].[((0:block).width<=[idx 0]0.085)(0)]" ], } +_DEBUG_GEOMETRIC_PREDICATES = defaultdict(list, _DEBUG_GEOMETRIC_PREDICATES) + +_DEBUG_VLM_PREDICATES = { + # Note: depending on CFG.burger_no_move_task_type, you might want to exclude + # some of the predicates here -- they won't be useful for the task and may + # cause problems in operator learning if labeled incorrectly. + "burger_no_move": [ + "Cooked0", + "Whole0", + "Cut0", + ], +} +_DEBUG_VLM_PREDICATES = defaultdict(list, _DEBUG_VLM_PREDICATES) @dataclass(frozen=True, eq=False, repr=False) class _DebugGrammar(_PredicateGrammar): """A grammar that generates only predicates starting with some string in - _DEBUG_PREDICATE_PREFIXES[CFG.env].""" + _DEBUG_GEOMETRIC_PREDICATES[CFG.env].""" base_grammar: _PredicateGrammar def generate(self, max_num: int) -> Dict[Predicate, float]: del max_num env_name = (CFG.env if not CFG.env.startswith("pybullet") else CFG.env[CFG.env.index("_") + 1:]) - expected_len = len(_DEBUG_PREDICATE_PREFIXES[env_name]) + expected_len = len(_DEBUG_GEOMETRIC_PREDICATES[env_name]) result = super().generate(expected_len) assert len(result) == expected_len return result @@ -491,7 +511,7 @@ def enumerate(self) -> Iterator[Tuple[Predicate, float]]: for (predicate, cost) in self.base_grammar.enumerate(): if any( str(predicate).startswith(debug_str) - for debug_str in _DEBUG_PREDICATE_PREFIXES[env_name]): + for debug_str in _DEBUG_GEOMETRIC_PREDICATES[env_name]): yield (predicate, cost) @@ -1021,14 +1041,25 @@ def learn_from_offline_dataset(self, dataset: Dataset) -> None: # Select a subset of the candidates to keep. logging.info("Selecting a subset...") if CFG.grammar_search_pred_selection_approach == "score_optimization": - # Create the score function that will be used to guide search. - score_function = create_score_function( - CFG.grammar_search_score_function, self._initial_predicates, - atom_dataset, candidates, self._train_tasks) - self._learned_predicates = \ - self._select_predicates_by_score_hillclimbing( - candidates, score_function, self._initial_predicates, - atom_dataset, self._train_tasks) + if CFG.grammar_search_select_all_debug and \ + CFG.grammar_search_use_handcoded_debug_grammar: + # Skip hill-climbing and select all the predicates from the + # debug grammar. + debug_predicate_names = _DEBUG_VLM_PREDICATES[ + CFG.env] + _DEBUG_GEOMETRIC_PREDICATES[CFG.env] + self._learned_predicates = set( + p for p in candidates.keys() + if p.name in debug_predicate_names) + else: + # Create the score function that will be used to guide search. + score_function = create_score_function( + CFG.grammar_search_score_function, + self._initial_predicates, atom_dataset, candidates, + self._train_tasks) + self._learned_predicates = \ + self._select_predicates_by_score_hillclimbing( + candidates, score_function, self._initial_predicates, + atom_dataset, self._train_tasks) elif CFG.grammar_search_pred_selection_approach == "clustering": self._learned_predicates = self._select_predicates_by_clustering( candidates, self._initial_predicates, dataset, atom_dataset) diff --git a/predicators/approaches/vlm_open_loop_approach.py b/predicators/approaches/vlm_open_loop_approach.py index 0bf9d23bb3..18da64e7d2 100644 --- a/predicators/approaches/vlm_open_loop_approach.py +++ b/predicators/approaches/vlm_open_loop_approach.py @@ -107,7 +107,10 @@ def learn_from_offline_dataset(self, dataset: Dataset) -> None: text_x = (width - text_width) / 2 text_y = height + 5 draw.text((text_x, text_y), text, font=font, fill="black") - self._prompt_state_imgs_list.append(draw._image) # pylint:disable=protected-access + # pylint:disable=protected-access + self._prompt_state_imgs_list.append( + draw._image) # type: ignore[attr-defined] + # pylint: enable=protected-access for action_num, action in enumerate(traj.actions): self._prompt_demos_str += f"Action {action_num}, from " + \ f"state {action_num} is {action.get_option()}\n" @@ -152,7 +155,10 @@ def _query_vlm_for_option_plan(self, task: Task) -> Sequence[_Option]: img_with_txt = utils.add_text_to_draw_img( draw, (50, 50), f"Initial state to plan from, Image {img_num}", img_font) - imgs_for_vlm.append(img_with_txt._image) # pylint:disable=protected-access + # pylint:disable=protected-access + imgs_for_vlm.append( + img_with_txt._image) # type: ignore[attr-defined] + # pylint: enable=protected-access options_str = "\n".join( str(opt) + ", params_space=" + str(opt.params_space) for opt in curr_options) diff --git a/predicators/datasets/generate_atom_trajs_with_vlm.py b/predicators/datasets/generate_atom_trajs_with_vlm.py index fb71ae4ecd..2cfe7e6d5f 100644 --- a/predicators/datasets/generate_atom_trajs_with_vlm.py +++ b/predicators/datasets/generate_atom_trajs_with_vlm.py @@ -2,11 +2,13 @@ import ast import glob +import itertools import logging import os import re import textwrap import traceback +from collections import defaultdict from concurrent.futures import ThreadPoolExecutor from functools import partial from inspect import getsource @@ -20,6 +22,7 @@ from predicators import utils from predicators.envs import BaseEnv +from predicators.envs.burger import BurgerEnv, BurgerNoMoveEnv from predicators.envs.vlm_envs import DUMMY_GOAL_OBJ_NAME from predicators.nsrt_learning.segmentation import _segment_with_option_changes from predicators.pretrained_model_interface import VisionLanguageModel @@ -107,9 +110,7 @@ def _generate_prompt_for_scene_labelling( for atom_str in atoms_list: prompt += f"\n{atom_str}" - if CFG.grammar_search_vlm_atom_label_prompt_type in [ - "img_option_diffs", "img_option_diffs_label_history" - ]: + if "img_option_diffs" in CFG.grammar_search_vlm_atom_label_prompt_type: # In this case, we need to load the 'per_scene_naive' prompt as well # for the first timestep. with open(filepath_prefix + "per_scene_naive.txt", @@ -134,13 +135,21 @@ def _generate_prompt_for_scene_labelling( curr_prompt_imgs = [ imgs_timestep[0] for imgs_timestep in traj.imgs[i - 1:i + 1] ] + if CFG.vlm_include_cropped_images: + if CFG.env in ["burger", "burger_no_move"]: # pragma: no cover + curr_prompt_imgs.extend([ + traj.cropped_imgs[i - 1][1], + traj.cropped_imgs[i - 1][0] + ]) + else: + raise NotImplementedError( + f"Cropped images not implemented for {CFG.env}.") curr_prompt += "\n\nSkill executed between states: " skill_name = traj.actions[i - 1].name + str( traj.actions[i - 1].objects) curr_prompt += skill_name - if CFG.grammar_search_vlm_atom_label_prompt_type == \ - "img_option_diffs_label_history": + if "label_history" in CFG.grammar_search_vlm_atom_label_prompt_type: curr_prompt += "\n\nPredicate values in the first scene, " \ "before the skill was executed: \n" curr_prompt += label_history[-1] @@ -185,11 +194,26 @@ def _label_single_trajectory_with_vlm_atom_values(indexed_traj: Tuple[ """Given a list of atoms, label every state in an ImageOptionTrajectory with the truth values of those atoms.""" idx, traj = indexed_traj + # Some of the atoms may be over objects that aren't in this trajectory, + # so filter those atoms out. + obj_names = [o.name for o in traj.objects] + filtered_atoms_list = [] + for a in atoms_list: + # Get the names of the objects in this atom. + atom_args = a[a.find('(') + 1:a.find(')')] + atom_objs = atom_args.split(',') + keep = True + for ao in atom_objs: + if ao not in obj_names: + keep = False + continue + if keep: + filtered_atoms_list.append(a) curr_scenes_labelled = 0 total_scenes_to_label = len(traj.imgs) curr_traj_txt_outputs: List[str] = [] prompts_for_traj = _generate_prompt_for_scene_labelling( - traj, atoms_list, label_history=curr_traj_txt_outputs) + traj, filtered_atoms_list, label_history=curr_traj_txt_outputs) for text_prompt, img_prompt in prompts_for_traj: # Sample VLM outputs with temperature 0 in an attempt to be # accurate. @@ -257,7 +281,20 @@ def _parse_unique_atom_proposals_from_list( objects that aren't known, (3) removing any duplicate atoms. """ atoms_strs_set = set() + all_atom_groundings = set() + unique_predicates = set() obj_names_set = set(obj.name for obj in relevant_objects_across_demos) + + # We'll use these mappings to generate VLM atoms for every possible + # grounding of each proposed predicate. + obj_name_to_type = { + obj.name: obj.type + for obj in relevant_objects_across_demos + } + type_to_obj_names = defaultdict(list) + for obj_name, _type in obj_name_to_type.items(): + type_to_obj_names[_type].append(obj_name) + num_atoms_considered = 0 for atoms_proposal_for_traj in atom_strs_proposals_list: assert len(atoms_proposal_for_traj) == 1 @@ -283,10 +320,29 @@ def _parse_unique_atom_proposals_from_list( break if atom_is_valid: atoms_strs_set.add(atom) + # Create VLM atoms for all other possible groundings of this + # atom's predicate. + types_in_atom = [obj_name_to_type[n] for n in obj_names_list] + names_matching_each_type = [ + type_to_obj_names[t] for t in types_in_atom + ] + combos = list(itertools.product(*names_matching_each_type)) + predicate = atom.split('(')[0] + unique_predicates.add(predicate) + # Note that this includes the original grounding. + other_groundings = [ + f"{predicate}({', '.join(c)})" for c in combos + ] + for og in other_groundings: + all_atom_groundings.add(og) logging.debug(f"Proposed atom: {atom} is valid: {atom_is_valid}") logging.info(f"VLM proposed a total of {num_atoms_considered} atoms.") logging.info(f"Of these, {len(atoms_strs_set)} were valid and unique.") - return atoms_strs_set + logging.info( + f"For the {len(unique_predicates)} predicates, there were " \ + f"{len(all_atom_groundings)} unique groundings." + ) + return all_atom_groundings def _save_labelled_trajs_as_txt( @@ -689,11 +745,11 @@ def _generate_ground_atoms_with_vlm_pure_visual_preds( atom_strs_proposals_list = _sample_vlm_atom_proposals_from_trajectories( image_option_trajs, vlm, 1) logging.info("Done querying VLM for candidate atoms!") - # We now parse and sanitize this set of atoms. - atom_proposals_set = _parse_unique_atom_proposals_from_list( - atom_strs_proposals_list, all_task_objs) - else: # pragma: no cover. - atom_proposals_set = env.get_vlm_debug_atom_strs(train_tasks) + else: # pragma: no cover + atom_strs_proposals_list = env.get_vlm_debug_atom_strs(train_tasks) + # We now parse and sanitize this set of atoms. + atom_proposals_set = _parse_unique_atom_proposals_from_list( + atom_strs_proposals_list, all_task_objs) assert len(atom_proposals_set) > 0, "Atom proposals set is empty!" # Given this set of unique atom proposals, we now ask the VLM # to label these in every scene from the demonstrations. @@ -947,7 +1003,7 @@ def create_ground_atom_data_from_generated_demos( first_iteration = False else: total_num_segment_states -= 1 # avoid double-counting states! - if total_num_segment_states != len(traj.states): # pragma: no cover. + if total_num_segment_states != len(traj.states): # pragma: no cover logging.info( ("WARNING: there are fewer total states after option-based " "segmentation than there are in the original trajectory. " @@ -958,16 +1014,75 @@ def create_ground_atom_data_from_generated_demos( # We assume that the state's simulator_state attribute is a dictionary, # and that the images for each state are accessed by the key "images". state_imgs: List[List[PIL.Image.Image]] = [] - for state in curr_traj_states_for_vlm: + cropped_state_imgs: List[List[PIL.Image.Image]] = [] + for i, state in enumerate(curr_traj_states_for_vlm): assert state.simulator_state is not None assert "images" in state.simulator_state + if CFG.vlm_include_cropped_images: + if CFG.env in ["burger", "burger_no_move"]: # pragma: no cover + assert isinstance(env, (BurgerEnv, BurgerNoMoveEnv)) + # For the non-initial states, get a cropped image that is a + # close-up of the relevant objects in the action that was + # taken. Assume the relevant objects are in the action's + # arguments. + prev_state = curr_traj_states_for_vlm[i - 1] + assert prev_state.simulator_state is not None + assert "images" in prev_state.simulator_state + relevant_states_for_crop = [state, prev_state] + if i != 0: + # Figure out which cells to include in the crop. + action = curr_traj_actions_for_vlm[i - 1].get_option() + min_col = env.num_cols + max_col = 0 + min_row = env.num_rows + max_row = 0 + # Get the (x, y) position of each relevant object. + for o in action.objects: + for s in relevant_states_for_crop: + row = s.get(o, "row") + col = s.get(o, "col") + min_col = int(min(min_col, col)) + max_col = int(max(max_col, col)) + min_row = int(min(min_row, row)) + max_row = int(max(max_row, row)) + # Count rows from the bottom rather than the top, + # because we actually want to index by traditional + # (x, y) in the numpy array. + temp = min_row + min_row = env.num_rows - 1 - max_row + max_row = env.num_rows - 1 - temp + # Assume that we can generate the intended crop from the + # first image in state.simulator_state["images"] + full_curr_img = state.simulator_state["images"][0] + full_prev_img = prev_state.simulator_state["images"][0] + approx_cell_size = full_curr_img.shape[ + 0] // env.num_rows + cropped_curr_img = full_curr_img[ + min_row * approx_cell_size:(max_row + 1) * + approx_cell_size, + min_col * approx_cell_size:(max_col + 1) * + approx_cell_size, :] + cropped_prev_img = full_prev_img[ + min_row * approx_cell_size:(max_row + 1) * + approx_cell_size, + min_col * approx_cell_size:(max_col + 1) * + approx_cell_size, :] + cropped_imgs = [ + PIL.Image.fromarray(img_arr) # type: ignore + for img_arr in + [cropped_curr_img, cropped_prev_img] + ] + cropped_state_imgs.append(cropped_imgs) + else: + raise NotImplementedError( + f"Cropped images not implemented for {CFG.env}.") state_imgs.append([ PIL.Image.fromarray(img_arr) # type: ignore for img_arr in state.simulator_state["images"] ]) img_option_trajs.append( ImageOptionTrajectory( - set(traj.states[0]), state_imgs, + set(traj.states[0]), state_imgs, cropped_state_imgs, [act.get_option() for act in curr_traj_actions_for_vlm], curr_traj_states_for_vlm, True, traj.train_task_idx)) option_segmented_trajs.append( @@ -1052,7 +1167,10 @@ def create_ground_atom_data_from_saved_img_trajs( vlm: Optional[VisionLanguageModel] = None) -> Dataset: """Given a folder containing trajectories that have images of scenes for each state, as well as options that transition between these states, output - a dataset.""" + a dataset. + + This method does not currently support including cropped images. + """ trajectories_folder_path = os.path.join( utils.get_path_to_predicators_root(), CFG.data_dir, CFG.vlm_trajs_folder_name) @@ -1152,7 +1270,7 @@ def create_ground_atom_data_from_saved_img_trajs( # Given ground options, we can finally make ImageOptionTrajectories. image_option_trajs.append( ImageOptionTrajectory(list(curr_task_objs), - img_traj, + img_traj, [], ground_option_traj, state_traj, _is_demo=True, diff --git a/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_burger.txt b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_burger.txt new file mode 100644 index 0000000000..4b08067b11 --- /dev/null +++ b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_burger.txt @@ -0,0 +1,3 @@ +You are a vision system for a robot provided with four images: a before image showing the state before a skill is executed, an after image showing the state after the skill is executed, and two cropped images highlighting objects relevant to the skill's arguments (the third image is a crop of the before image, and the fourth image is a crop of the after image). You are also given a list of predicates and their values in the before image. Your task is to output the values of these predicates in the after image. First, describe the expected visual changes based on the executed skill, explicitly noting the skill performed. Second, describe the visual changes you observe between the before and after images, focusing on the objects involved in the skill's arguments. Finally, list each predicate value in the after image in the format `: . `, and separately list the predicates that changed. If an object is not visible in the cropped images, check the uncropped images. If you still cannot locate the object involved in a specific predicate, retain the predicate’s value from the previous timestep. Note that when a cook action is taken on any kind of food that is on the grill, it does not take time to finish cooking -- it cooks instantly. Note that just placing any kind of food on the grill without explicitly cooking it does not make it cooked. + +Predicates: \ No newline at end of file diff --git a/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history_crops.txt b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history_crops.txt new file mode 100644 index 0000000000..fcb5edff30 --- /dev/null +++ b/predicators/datasets/vlm_input_data_prompts/atom_labelling/img_option_diffs_label_history_crops.txt @@ -0,0 +1,3 @@ +You are a vision system for a robot provided with four images: a before image showing the state before a skill is executed, an after image showing the state after the skill is executed, and two cropped images highlighting objects relevant to the skill's arguments (the third image is a crop of the before image, and the fourth image is a crop of the after image). You are also given a list of predicates and their values in the before image. Your task is to output the values of these predicates in the after image. First, describe the expected visual changes based on the executed skill, explicitly noting the skill performed. Second, describe the visual changes you observe between the before and after images, focusing on the objects involved in the skill's arguments. Finally, list each predicate value in the after image in the format `: . `, and separately list the predicates that changed. If an object is not visible in the cropped images, check the uncropped images. 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z2*B!yWyXEg+p~u+&Q!}le7KjyQlsUDTH#_!^+u)vp(HDf}Ps0 z_y*74*4&(DU#9%Md)joXU;3`aHk< r@h8F}{I}vWJox`*{_o3X?g_<1>B{Z3zYfU@#79x~qfEJ!Nx=UAqz(E{ literal 0 HcmV?d00001 diff --git a/predicators/envs/base_env.py b/predicators/envs/base_env.py index b9e3c31032..b77cab93ee 100644 --- a/predicators/envs/base_env.py +++ b/predicators/envs/base_env.py @@ -382,7 +382,8 @@ def get_observation(self) -> Observation: assert isinstance(self._current_observation, State) return self._current_observation.copy() - def get_vlm_debug_atom_strs(self, train_tasks: List[Task]) -> Set[str]: + def get_vlm_debug_atom_strs(self, + train_tasks: List[Task]) -> List[List[str]]: """A 'debug grammar' set of predicates that should be sufficient for completing the task; useful for comparing different methods of VLM truth-value labelling given the same set of atom proposals to label. @@ -402,4 +403,7 @@ def get_vlm_debug_atom_strs(self, train_tasks: List[Task]) -> Set[str]: ", ".join([o.name for o in atom.objects]) + ")" for atom in sorted(all_ground_atoms_set) } - return atom_strs + # We return the atom strings in this format to match the format they are + # outputted in when querying the VLM. That way, we can use the same + # function to sanitize atoms regardless of their origin. + return [[a] for a in atom_strs] diff --git a/predicators/envs/burger.py b/predicators/envs/burger.py index 3b4e9ba06d..3ca54d2868 100644 --- a/predicators/envs/burger.py +++ b/predicators/envs/burger.py @@ -8,6 +8,7 @@ import copy import io import logging +from collections import defaultdict from typing import Callable, List, Optional, Sequence, Set, Tuple import matplotlib @@ -21,8 +22,8 @@ from predicators.envs import BaseEnv from predicators.settings import CFG from predicators.structs import Action, DefaultEnvironmentTask, \ - EnvironmentTask, GroundAtom, Object, Observation, Predicate, State, Type, \ - Video + EnvironmentTask, GroundAtom, Object, Observation, Predicate, State, Task, \ + Type, Video class BurgerEnv(BaseEnv): @@ -56,12 +57,15 @@ class BurgerEnv(BaseEnv): # Types _object_type = Type("object", []) + _item_or_station_type = Type("item_or_station", [], _object_type) + # _item_type = Type("item", [], _item_or_station_type) + # _station_type = Type("station", [], _item_or_station_type) _item_type = Type("item", [], _object_type) _station_type = Type("station", [], _object_type) _robot_type = Type("robot", ["row", "col", "z", "fingers", "dir"], _object_type) _patty_type = Type("patty", ["row", "col", "z"], _item_type) - _tomato_type = Type("tomato", ["row", "col", "z"], _item_type) + _tomato_type = Type("lettuce", ["row", "col", "z"], _item_type) _cheese_type = Type("cheese", ["row", "col", "z"], _item_type) _bottom_bun_type = Type("bottom_bun", ["row", "col", "z"], _item_type) _top_bun_type = Type("top_bun", ["row", "col", "z"], _item_type) @@ -72,8 +76,10 @@ class BurgerEnv(BaseEnv): dir_to_enum = {"up": 0, "left": 1, "down": 2, "right": 3} enum_to_dir = {0: "up", 1: "left", 2: "down", 3: "right"} - num_rows = CFG.gridworld_num_rows - num_cols = CFG.gridworld_num_cols + # Currently, a lot of the code for this env -- like the rendering -- assumes + # a 5x5 grid. + num_rows = 5 + num_cols = 5 def __init__(self, use_gui: bool = True) -> None: super().__init__(use_gui) @@ -104,6 +110,12 @@ def __init__(self, use_gui: bool = True) -> None: self._On_holds) self._OnNothing = Predicate("OnNothing", [self._object_type], self._OnNothing_holds) + # When an argument should be an item or a station but not a robot, + # having the argument more narrowly scoped than the object type would be + # helpful. But this is causing errors so we'll avoid it for now. + # # self._Clear = Predicate("Clear", [self._item_or_station_type], + # self._Clear_holds) + # TODO(ashay): fix the errors associated with this. self._Clear = Predicate("Clear", [self._object_type], self._Clear_holds) self._GoalHack = Predicate("GoalHack", [ @@ -208,8 +220,9 @@ def get_edge_cells_for_object_placement( return ret - def _get_tasks(self, num: int, - rng: np.random.Generator) -> List[EnvironmentTask]: + def _get_tasks(self, num: int, rng: np.random.Generator, + train_or_test: str) -> List[EnvironmentTask]: + del train_or_test # unused tasks = [] state_dict = {} hidden_state = {} @@ -242,7 +255,7 @@ def _get_tasks(self, num: int, # Add tomato r, c = shuffled_spots[4] - tomato = Object("tomato", self._tomato_type) + tomato = Object("lettuce", self._tomato_type) state_dict[tomato] = {"row": r, "col": c, "z": 0} hidden_state[tomato] = {"is_sliced": 0.0, "is_held": 0.0} @@ -264,6 +277,8 @@ def _get_tasks(self, num: int, state_dict[bottom_bun] = {"row": r, "col": c, "z": 0} hidden_state[bottom_bun] = {"is_held": 0.0} + # Note that the test task differs from the train task only in the + # positions of objects. goal = { GroundAtom(self._On, [patty, bottom_bun]), GroundAtom(self._On, [cheese, patty]), @@ -279,7 +294,6 @@ def _get_tasks(self, num: int, GroundAtom(self._GoalHack, [bottom_bun, patty, cheese, tomato, top_bun]) } - state = utils.create_state_from_dict(state_dict) state.simulator_state = {} state.simulator_state["state"] = hidden_state @@ -294,10 +308,14 @@ def _get_tasks(self, num: int, return tasks def _generate_train_tasks(self) -> List[EnvironmentTask]: - return self._get_tasks(num=CFG.num_train_tasks, rng=self._train_rng) + return self._get_tasks(num=CFG.num_train_tasks, + rng=self._train_rng, + train_or_test="train") def _generate_test_tasks(self) -> List[EnvironmentTask]: - return self._get_tasks(num=CFG.num_test_tasks, rng=self._test_rng) + return self._get_tasks(num=CFG.num_test_tasks, + rng=self._test_rng, + train_or_test="test") @classmethod def Adjacent_holds(cls, state: State, objects: Sequence[Object]) -> bool: @@ -364,10 +382,16 @@ def _Holding_holds(self, state: State, objects: Sequence[Object]) -> bool: def _On_holds(self, state: State, objects: Sequence[Object]) -> bool: a, b = objects + assert state.simulator_state is not None + assert "state" in state.simulator_state ax, ay = self.get_position(a, state) bx, by = self.get_position(b, state) az = state.get(a, "z") bz = state.get(b, "z") + # If an object is held by the robot, the object is not on the robot. + if a.is_instance(self._item_type): + if state.simulator_state["state"][a]["is_held"] > 0.5: + return False return ax == bx and ay == by and az - 1 == bz def _OnNothing_holds(self, state: State, @@ -382,6 +406,13 @@ def _OnNothing_holds(self, state: State, def _Clear_holds(self, state: State, objects: Sequence[Object]) -> bool: obj, = objects + assert state.simulator_state is not None + assert "state" in state.simulator_state + # A held object is not clear. + if obj.is_instance( + self._item_type + ) and state.simulator_state["state"][obj]["is_held"] > 0.5: + return False for other_obj in state: if other_obj.is_instance(self._item_type) or other_obj.is_instance( self._station_type): @@ -429,7 +460,6 @@ def predicates(self) -> Set[Predicate]: @property def goal_predicates(self) -> Set[Predicate]: return {self._On, self._IsCooked, self._IsSliced} - # return {self._On, self._GoalHack} @property def agent_goal_predicates(self) -> Set[Predicate]: @@ -564,7 +594,7 @@ def simulate(self, state: State, action: Action) -> State: next_state.simulator_state["state"][on_top]["is_held"] = 1.0 next_state.set(on_top, "col", rx) next_state.set(on_top, "row", ry) - next_state.set(on_top, "z", 0) + next_state.set(on_top, "z", 1) next_state.set(self._robot, "fingers", 1.0) # Handle placing. @@ -581,7 +611,7 @@ def simulate(self, state: State, action: Action) -> State: next_state.set(held_item, "col", px) next_state.set(held_item, "row", py) # If any other objects are at this location, then this must go - # on top of them. + # on top of them. Otherwise, we are placing on the ground. objects_at_loc = [] for obj in other_objects: ox, oy = self.get_position(obj, state) @@ -610,8 +640,8 @@ def render_state_plt( # The DPI has to be sufficiently high otherwise when the matplotlib # figure gets converted to a PIL image, text in the image can become # blurry. - fig, ax = plt.subplots(1, 1, figsize=figsize, dpi=200) - plt.suptitle(caption, wrap=True) + fig, ax = plt.subplots(1, 1, figsize=figsize, dpi=216) + fontsize = 14 # Plot vertical lines for i in range(self.num_cols + 1): @@ -626,7 +656,7 @@ def render_state_plt( robot_direction = self.enum_to_dir[state.get(self._robot, "dir")] robot_img = mpimg.imread( utils.get_env_asset_path(f"imgs/robot_{robot_direction}.png")) - img_size = (0.8, 0.8) + img_size = (0.7, 0.7) ax.imshow(robot_img, extent=[ x + (1 - img_size[0]) / 2, x + (1 + img_size[0]) / 2, @@ -636,7 +666,7 @@ def render_state_plt( ax.text(x + 1 / 2, y + (1 - img_size[1]) / 2, self._robot.name, - fontsize=10, + fontsize=fontsize, color="red", ha="center", va="top", @@ -652,7 +682,7 @@ def render_state_plt( ax.text(x + 1 / 2, y + (1 - img_size[1]) / 2, self._grill.name, - fontsize=10, + fontsize=fontsize, color="red", ha="center", va="top", @@ -661,21 +691,22 @@ def render_state_plt( boxstyle="square,pad=0.0")) # Draw cutting board - x, y = self.get_position(self._cutting_board, state) - cutting_board_img = mpimg.imread( - utils.get_env_asset_path("imgs/cutting_board.png")) - ax.imshow(cutting_board_img, extent=[x, x + 1, y, y + 1]) - if CFG.burger_render_set_of_marks: - ax.text(x + 1 / 2, - y + (1 - img_size[1]) / 2, - self._cutting_board.name, - fontsize=10, - color="red", - ha="center", - va="top", - bbox=dict(facecolor="black", - alpha=0.5, - boxstyle="square,pad=0.0")) + if self._cutting_board in state: + x, y = self.get_position(self._cutting_board, state) + cutting_board_img = mpimg.imread( + utils.get_env_asset_path("imgs/cutting_board.png")) + ax.imshow(cutting_board_img, extent=[x, x + 1, y, y + 1]) + if CFG.burger_render_set_of_marks: + ax.text(x + 1 / 2, + y + (1 - img_size[1]) / 2, + self._cutting_board.name, + fontsize=fontsize, + color="red", + ha="center", + va="top", + bbox=dict(facecolor="black", + alpha=0.5, + boxstyle="square,pad=0.0")) # Draw items type_to_img = { @@ -686,9 +717,10 @@ def render_state_plt( self._cheese_type: mpimg.imread("predicators/envs/assets/imgs/cheese.png"), self._tomato_type: - mpimg.imread(utils.get_env_asset_path("imgs/whole_tomato.png")), + mpimg.imread(utils.get_env_asset_path("imgs/uncut_lettuce.png")), self._patty_type: - mpimg.imread(utils.get_env_asset_path("imgs/raw_patty.png")) + mpimg.imread( + utils.get_env_asset_path("imgs/realistic_raw_patty_full.png")) } held_img_size = (0.3, 0.3) offset = held_img_size[1] * (1 / 3) @@ -700,11 +732,12 @@ def render_state_plt( if "is_cooked" in state.simulator_state["state"][ item] and self._IsCooked_holds(state, [item]): img = mpimg.imread( - utils.get_env_asset_path("imgs/cooked_patty.png")) + utils.get_env_asset_path( + "imgs/realistic_cooked_patty_full.png")) elif "is_sliced" in state.simulator_state["state"][ item] and self._IsSliced_holds(state, [item]): img = mpimg.imread( - utils.get_env_asset_path("imgs/sliced_tomato.png")) + utils.get_env_asset_path("imgs/cut_lettuce.png")) zorder = state.get(item, "z") is_held = state.simulator_state["state"][item]["is_held"] > 0.5 x, y = self.get_position(item, state) @@ -736,25 +769,45 @@ def render_state_plt( ax.imshow(img, extent=extent, zorder=zorder) if CFG.burger_render_set_of_marks: if is_held: - ax.text(x + (1 + held_img_size[0]) * (1 / 2), + rx, _ = self.get_position(self._robot, state) + # If the robot is on the right edge, put text labels for + # held items on the left side so that they don't extend past + # the edge of the grid and make the image larger. + if rx == self.num_cols - 1: + horizontal_align = "right" + text_x = x + (1 - held_img_size[0]) * (1 / 2) + else: + horizontal_align = "left" + text_x = x + (1 + held_img_size[0]) * (1 / 2) + ax.text(text_x, y + offset + held_img_size[1] / 2, item.name, - fontsize=10, + fontsize=fontsize, color="red", - ha="left", + ha=horizontal_align, va="top", bbox=dict(facecolor="black", alpha=0.5, boxstyle="square,pad=0.0")) else: if zorder > 0: - if self._On_holds( - state, [item, self._grill]) or self._On_holds( - state, [item, self._cutting_board]): + # If the item is on the grill or cutting board, and + # there is not an item on top of it, then put its text + # label near the top of the cell. + if self._cutting_board in state: + check = (self._On_holds(state, [item, self._grill]) + or self._On_holds(state, [ + item, self._cutting_board + ])) and self._Clear_holds(state, [item]) + else: + check = self._On_holds(state, [ + item, self._grill + ]) and self._Clear_holds(state, [item]) + if check: ax.text(x + 1 / 2, y + (1 + img_size[1]) / 2, item.name, - fontsize=10, + fontsize=fontsize, color="red", ha="center", va="bottom", @@ -765,7 +818,7 @@ def render_state_plt( ax.text(x, y + (0.1 * zorder) + (1 - img_size[1]) / 2, item.name, - fontsize=10, + fontsize=fontsize, color="red", ha="left", va="top", @@ -773,13 +826,16 @@ def render_state_plt( alpha=0.5, boxstyle="square,pad=0.0")) else: + # If something is on top of this item or this item is on + # something else, then put the text label on the left + # side of the cell. if not self._Clear_holds( state, [item]) or not self._OnNothing_holds( state, [item]): ax.text(x, y + (1 - img_size[1]) / 2, item.name, - fontsize=10, + fontsize=fontsize, color="red", ha="left", va="top", @@ -790,7 +846,7 @@ def render_state_plt( ax.text(x + 1 / 2, y + (1 - img_size[1]) / 2, item.name, - fontsize=10, + fontsize=fontsize, color="red", ha="center", va="top", @@ -817,10 +873,12 @@ def render_state(self, task: EnvironmentTask, action: Optional[Action] = None, caption: Optional[str] = None) -> Video: + if CFG.burger_dummy_render: + return [np.zeros((16, 16), dtype=np.uint8)] fig = self.render_state_plt(state, task, action, caption) # Create an in-memory binary stream. buf = io.BytesIO() - fig.savefig(buf, format='png') + fig.savefig(buf, format='png', bbox_inches='tight', pad_inches=0) # Rewind the stream to the beginning so that it can be read from. buf.seek(0) img = Image.open(buf) @@ -841,6 +899,10 @@ def render_state(self, ret_img = copy.deepcopy(jpeg_img) ret_arr = np.array(ret_img) + # import PIL.Image + # temp = PIL.Image.fromarray(ret_arr) + # import pdb; pdb.set_trace() + buf.close() jpeg_buf.close() return [ret_arr] @@ -908,3 +970,554 @@ def _event_to_action(state: State, return action return _event_to_action + + +class BurgerNoMoveEnv(BurgerEnv): + """BurgerEnv but with the movement option wrapped inside each of the other + options.""" + + def __init__(self, use_gui: bool = True) -> None: + super().__init__(use_gui) + self._OnGround = Predicate("OnGround", [self._item_type], + self._OnGround_holds) + self._GoalHack2 = Predicate("GoalHack2", + [self._bottom_bun_type, self._patty_type], + self._GoalHack2_holds) + self._GoalHack3 = Predicate("GoalHack3", + [self._bottom_bun_type, self._tomato_type], + self._GoalHack3_holds) + self._GoalHack4 = Predicate("GoalHack4", + [self._patty_type, self._tomato_type], + self._GoalHack3_holds) + self._GoalHack5 = Predicate( + "GoalHack5", [self._cutting_board_type, self._patty_type], + self._GoalHack5_holds) + self._GoalHack6 = Predicate("GoalHack6", + [self._patty_type, self._patty_type], + self._GoalHack5_holds) + self._GoalHack7 = Predicate("GoalHack7", + [self._grill_type, self._patty_type], + self._GoalHack5_holds) + + def _OnGround_holds(self, state: State, objects: Sequence[Object]) -> bool: + obj, = objects + obj_z = state.get(obj, "z") + return obj_z == 0 + + def _GoalHack2_holds(self, state: State, + objects: Sequence[Object]) -> bool: + # The object is somewhere below the patty and the patty is cooked. + obj, patty = objects + obj_z = state.get(obj, "z") + obj_x, obj_y = self.get_position(obj, state) + p_z = state.get(patty, "z") + p_x, p_y = self.get_position(patty, state) + same_cell = obj_x == p_x and obj_y == p_y + return obj_z < p_z and same_cell and self._IsCooked_holds( + state, [patty]) + + def _GoalHack3_holds(self, state: State, + objects: Sequence[Object]) -> bool: + # The object is somewhere below the tomato and the tomato is sliced. + obj, tomato = objects + obj_z = state.get(obj, "z") + obj_x, obj_y = self.get_position(obj, state) + t_z = state.get(tomato, "z") + t_x, t_y = self.get_position(tomato, state) + same_cell = obj_x == t_x and obj_y == t_y + return obj_z < t_z and same_cell and self._IsSliced_holds( + state, [tomato]) + + def _GoalHack5_holds(self, state: State, + objects: Sequence[Object]) -> bool: + obj, patty = objects + # The object is right below the patty and the patty is cooked. + obj_z = state.get(obj, "z") + obj_x, obj_y = self.get_position(obj, state) + p_z = state.get(patty, "z") + p_x, p_y = self.get_position(patty, state) + same_cell = obj_x == p_x and obj_y == p_y + # We check that the object is right below the patty because we don't + # want this turning on for the top patty when there is a stack of two + # cooked patties on a cutting board -- then the operator that covers + # this transition will be less general. + return obj_z == p_z - 1 and same_cell and self._IsCooked_holds( + state, [patty]) + + @classmethod + def get_name(cls) -> str: + return "burger_no_move" + + def get_edge_cells_for_object_placement( + self, rng: np.random.Generator) -> List[Tuple[int, int]]: + del rng # unused + n_row = self.num_rows + n_col = self.num_cols + top = [(n_row - 1, col) for col in range(n_col)] + left = [(row, 0) for row in range(n_row)] + bottom = [(0, col) for col in range(n_col)] + right = [(row, n_col - 1) for row in range(n_row)] + corners = [(0, 0), (0, self.num_cols - 1), (self.num_rows - 1, 0), + (self.num_rows - 1, self.num_cols - 1)] + cells = (set(top) | set(left) | set(bottom) + | set(right)) - set(corners) + return sorted(cells) + + def _get_tasks(self, num: int, rng: np.random.Generator, + train_or_test: str) -> List[EnvironmentTask]: + spots_for_objects = self.get_edge_cells_for_object_placement(rng) + + def create_default_state() -> Tuple[dict, dict, List[Tuple[int, int]]]: + state_dict = {} + hidden_state = {} + shuffled_spots = spots_for_objects.copy() + rng.shuffle(shuffled_spots) + + # Add robot, grill, and cutting board + state_dict[self._robot] = { + "row": 2, # assumes 5x5 grid + "col": 2, # assumes 5x5 grid + "z": 0, + "fingers": 0.0, + "dir": 3 + } + r, c = shuffled_spots[0] + state_dict[self._grill] = {"row": r, "col": c, "z": 0} + r, c = shuffled_spots[1] + state_dict[self._cutting_board] = {"row": r, "col": c, "z": 0} + + # Add patty + r, c = shuffled_spots[2] + patty = Object("patty1", self._patty_type) + state_dict[patty] = {"row": r, "col": c, "z": 0} + hidden_state[patty] = {"is_cooked": 0.0, "is_held": 0.0} + + # Add tomato + r, c = shuffled_spots[3] + tomato = Object("lettuce1", self._tomato_type) + state_dict[tomato] = {"row": r, "col": c, "z": 0} + hidden_state[tomato] = {"is_sliced": 0.0, "is_held": 0.0} + + # Add cheese + r, c = shuffled_spots[4] + cheese = Object("cheese1", self._cheese_type) + state_dict[cheese] = {"row": r, "col": c, "z": 0} + hidden_state[cheese] = {"is_held": 0.0} + + # Add top bun + r, c = shuffled_spots[5] + top_bun = Object("top_bun1", self._top_bun_type) + state_dict[top_bun] = {"row": r, "col": c, "z": 0} + hidden_state[top_bun] = {"is_held": 0.0} + + # Add bottom bun + r, c = shuffled_spots[6] + bottom_bun = Object("bottom_bun1", self._bottom_bun_type) + state_dict[bottom_bun] = {"row": r, "col": c, "z": 0} + hidden_state[bottom_bun] = {"is_held": 0.0} + + return state_dict, hidden_state, shuffled_spots + + def create_task(state_dict: dict, hidden_state: dict, + goal: Set[GroundAtom], + alt_goal: Set[GroundAtom]) -> EnvironmentTask: + state = utils.create_state_from_dict(state_dict) + state.simulator_state = {} + state.simulator_state["state"] = hidden_state + # A DefaultEnvironmentTask is a dummy environment task. Our render + # function does not use the task argument, so this is ok. + state.simulator_state["images"] = self.render_state( + state, DefaultEnvironmentTask) + # PIL.Image.fromarray(state.simulator_state["images"][0]).show() + # import pdb; pdb.set_trace() + return EnvironmentTask(state, goal, alt_goal_desc=alt_goal) + + def name_to_obj(state_dict: dict) -> dict[str, Object]: + d = {} + for obj in state_dict.keys(): + d[obj.name] = obj + return d + + # We'll have three kinds of train/test pairs. + # Consider a burger as a bottom bun, a top bun, and something(s) in + # between them. + # (1) + # Train: + # - Make a burger with a cooked patty + # - Make a burger with chopped lettuce + # - Place chopped lettuce on a patty + # - Place chopped lettuce on a patty, place patty on cutting board + # Test: + # - Make a burger with a cooked patty and chopped lettuce + # (2) + # Train: + # - Make a burger with a cooked patty + # - Cook two patties and stack them, place bottom patty on cutting + # board + # Test: + # - Make a burger with two cooked patties + # (3) + # Train: + # - Make a burger with a cooked patty + # Test: + # - Make several bottom bun + cooked patty stacks + + def create_tasks_for_type_one( + ) -> Tuple[List[EnvironmentTask], List[EnvironmentTask]]: + train_tasks = [] + test_tasks = [] + + # train task 1 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + bottom_bun = d["bottom_bun1"] + patty = d["patty1"] + top_bun = d["top_bun1"] + train_goal = { + GroundAtom(self._IsCooked, [patty]), + GroundAtom(self._On, [patty, bottom_bun]), + GroundAtom(self._On, [top_bun, patty]) + } + alt_train_goal = { + GroundAtom(self._GoalHack2, [bottom_bun, patty]), + GroundAtom(self._On, [top_bun, patty]) + } + train_task = create_task(state_dict, hidden_state, train_goal, + alt_train_goal) + train_tasks.append(train_task) + + # train task 2 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + bottom_bun = d["bottom_bun1"] + tomato = d["lettuce1"] + top_bun = d["top_bun1"] + train_goal = { + GroundAtom(self._IsSliced, [tomato]), + GroundAtom(self._On, [tomato, bottom_bun]), + GroundAtom(self._On, [top_bun, tomato]) + } + alt_train_goal = { + GroundAtom(self._GoalHack3, [bottom_bun, tomato]), + GroundAtom(self._On, [top_bun, tomato]) + } + train_task = create_task(state_dict, hidden_state, train_goal, + alt_train_goal) + train_tasks.append(train_task) + + # train task 3 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + patty = d["patty1"] + tomato = d["lettuce1"] + train_goal = { + GroundAtom(self._On, [patty, self._cutting_board]), + GroundAtom(self._On, [tomato, patty]), + GroundAtom(self._IsSliced, [tomato]) + } + alt_train_goal = { + GroundAtom(self._On, [patty, self._cutting_board]), + GroundAtom(self._GoalHack4, [patty, tomato]) + } + train_task = create_task(state_dict, hidden_state, train_goal, + alt_train_goal) + train_tasks.append(train_task) + + # train task 4 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + patty = d["patty1"] + tomato = d["lettuce1"] + # Start with the patty on the grill + r, c = shuffled_spots[0] # where the grill is + state_dict[patty] = {"row": r, "col": c, "z": 1} + hidden_state[patty] = {"is_cooked": 0.0, "is_held": 0.0} + train_goal = { + GroundAtom(self._On, [patty, self._grill]), + GroundAtom(self._On, [tomato, patty]), + GroundAtom(self._IsSliced, [tomato]) + } + alt_train_goal = { + GroundAtom(self._On, [patty, self._grill]), + GroundAtom(self._GoalHack4, [patty, tomato]) + } + train_task = create_task(state_dict, hidden_state, train_goal, + alt_train_goal) + train_tasks.append(train_task) + + # test task 1 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + bottom_bun = d["bottom_bun1"] + patty = d["patty1"] + tomato = d["lettuce1"] + top_bun = d["top_bun1"] + test_goal = { + GroundAtom(self._IsCooked, [patty]), + GroundAtom(self._IsSliced, [tomato]), + GroundAtom(self._On, [patty, bottom_bun]), + GroundAtom(self._On, [tomato, patty]), + GroundAtom(self._On, [top_bun, tomato]) + } + alt_test_goal = { + GroundAtom(self._GoalHack2, [bottom_bun, patty]), + GroundAtom(self._GoalHack4, [patty, tomato]), + GroundAtom(self._On, [top_bun, tomato]) + } + test_task = create_task(state_dict, hidden_state, test_goal, + alt_test_goal) + test_tasks.append(test_task) + + return train_tasks, test_tasks + + def create_tasks_for_type_two( + ) -> Tuple[List[EnvironmentTask], List[EnvironmentTask]]: + train_tasks = [] + test_tasks = [] + + # train task 1 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + bottom_bun = d["bottom_bun1"] + patty = d["patty1"] + top_bun = d["top_bun1"] + train_goal = { + GroundAtom(self._IsCooked, [patty]), + GroundAtom(self._On, [patty, bottom_bun]), + GroundAtom(self._On, [top_bun, patty]) + } + alt_train_goal = { + GroundAtom(self._GoalHack2, [bottom_bun, patty]), + GroundAtom(self._On, [top_bun, patty]) + } + train_task = create_task(state_dict, hidden_state, train_goal, + alt_train_goal) + train_tasks.append(train_task) + + # train task 2 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + patty = d["patty1"] + r, c = shuffled_spots[7] # next empty cell + patty2 = Object("patty2", self._patty_type) + state_dict[patty2] = {"row": r, "col": c, "z": 0} + hidden_state[patty2] = {"is_cooked": 0.0, "is_held": 0.0} + train_goal = { + GroundAtom(self._On, [patty, self._cutting_board]), + GroundAtom(self._IsCooked, [patty]), + GroundAtom(self._IsCooked, [patty2]), + GroundAtom(self._On, [patty2, patty]) + } + alt_train_goal = { + GroundAtom(self._GoalHack5, [self._cutting_board, patty]), + GroundAtom(self._GoalHack6, [patty, patty2]) + } + train_task = create_task(state_dict, hidden_state, train_goal, + alt_train_goal) + train_tasks.append(train_task) + + # train task 3 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + patty = d["patty1"] + r, c = shuffled_spots[7] # next empty cell + patty2 = Object("patty2", self._patty_type) + state_dict[patty2] = {"row": r, "col": c, "z": 0} + hidden_state[patty2] = {"is_cooked": 0.0, "is_held": 0.0} + train_goal = { + GroundAtom(self._On, [patty2, self._grill]), + GroundAtom(self._IsCooked, [patty]), + GroundAtom(self._IsCooked, [patty2]), + GroundAtom(self._On, [patty, patty2]) + } + alt_train_goal = { + GroundAtom(self._GoalHack7, [self._grill, patty2]), + GroundAtom(self._GoalHack6, [patty2, patty]) + } + train_task = create_task(state_dict, hidden_state, train_goal, + alt_train_goal) + train_tasks.append(train_task) + + # test task 1 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + bottom_bun = d["bottom_bun1"] + patty = d["patty1"] + top_bun = d["top_bun1"] + r, c = shuffled_spots[7] # next empty cell + patty2 = Object("patty2", self._patty_type) + state_dict[patty2] = {"row": r, "col": c, "z": 0} + hidden_state[patty2] = {"is_cooked": 0.0, "is_held": 0.0} + test_goal = { + GroundAtom(self._IsCooked, [patty]), + GroundAtom(self._On, [patty, bottom_bun]), + GroundAtom(self._On, [patty2, patty]), + GroundAtom(self._On, [top_bun, patty2]) + } + alt_test_goal = { + GroundAtom(self._GoalHack2, [bottom_bun, patty]), + GroundAtom(self._GoalHack6, [patty, patty2]), + GroundAtom(self._On, [top_bun, patty2]) + } + test_task = create_task(state_dict, hidden_state, test_goal, + alt_test_goal) + test_tasks.append(test_task) + + return train_tasks, test_tasks + + def create_tasks_for_type_three( + ) -> Tuple[List[EnvironmentTask], List[EnvironmentTask]]: + train_tasks = [] + test_tasks = [] + + # train task 1 + state_dict, hidden_state, shuffled_spots = create_default_state() + d = name_to_obj(state_dict) + bottom_bun = d["bottom_bun1"] + patty = d["patty1"] + top_bun = d["top_bun1"] + train_goal = { + GroundAtom(self._IsCooked, [patty]), + GroundAtom(self._On, [patty, bottom_bun]), + GroundAtom(self._On, [top_bun, patty]) + } + alt_train_goal = { + GroundAtom(self._GoalHack2, [bottom_bun, patty]), + GroundAtom(self._On, [top_bun, patty]) + } + train_task = create_task(state_dict, hidden_state, train_goal, + alt_train_goal) + train_tasks.append(train_task) + + # test task 1 + state_dict, hidden_state, shuffled_spots = create_default_state() + state_dict = { + self._robot: state_dict[self._robot], + self._grill: state_dict[self._grill] + } + hidden_state = {} + # Create three patties. + r, c = shuffled_spots[1] + patty = Object("patty1", self._patty_type) + state_dict[patty] = {"row": r, "col": c, "z": 0} + hidden_state[patty] = {"is_cooked": 0.0, "is_held": 0.0} + r, c = shuffled_spots[2] + patty2 = Object("patty2", self._patty_type) + state_dict[patty2] = {"row": r, "col": c, "z": 0} + hidden_state[patty2] = {"is_cooked": 0.0, "is_held": 0.0} + r, c = shuffled_spots[3] + patty3 = Object("patty3", self._patty_type) + state_dict[patty3] = {"row": r, "col": c, "z": 0} + hidden_state[patty3] = {"is_cooked": 0.0, "is_held": 0.0} + r, c = shuffled_spots[4] + patty4 = Object("patty4", self._patty_type) + state_dict[patty4] = {"row": r, "col": c, "z": 0} + hidden_state[patty4] = {"is_cooked": 0.0, "is_held": 0.0} + r, c = shuffled_spots[5] + patty5 = Object("patty5", self._patty_type) + state_dict[patty5] = {"row": r, "col": c, "z": 0} + hidden_state[patty5] = {"is_cooked": 0.0, "is_held": 0.0} + # Create three bottom buns. + r, c = shuffled_spots[6] + bottom_bun = Object("bottom_bun1", self._bottom_bun_type) + state_dict[bottom_bun] = {"row": r, "col": c, "z": 0} + hidden_state[bottom_bun] = {"is_held": 0.0} + r, c = shuffled_spots[7] + bottom_bun2 = Object("bottom_bun2", self._bottom_bun_type) + state_dict[bottom_bun2] = {"row": r, "col": c, "z": 0} + hidden_state[bottom_bun2] = {"is_held": 0.0} + r, c = shuffled_spots[8] + bottom_bun3 = Object("bottom_bun3", self._bottom_bun_type) + state_dict[bottom_bun3] = {"row": r, "col": c, "z": 0} + hidden_state[bottom_bun3] = {"is_held": 0.0} + r, c = shuffled_spots[9] + bottom_bun4 = Object("bottom_bun4", self._bottom_bun_type) + state_dict[bottom_bun4] = {"row": r, "col": c, "z": 0} + hidden_state[bottom_bun4] = {"is_held": 0.0} + r, c = shuffled_spots[10] + bottom_bun5 = Object("bottom_bun5", self._bottom_bun_type) + state_dict[bottom_bun5] = {"row": r, "col": c, "z": 0} + hidden_state[bottom_bun5] = {"is_held": 0.0} + + test_goal = { + GroundAtom(self._On, [patty, bottom_bun]), + GroundAtom(self._On, [patty2, bottom_bun2]), + GroundAtom(self._On, [patty3, bottom_bun3]), + GroundAtom(self._On, [patty4, bottom_bun4]), + GroundAtom(self._On, [patty5, bottom_bun5]), + GroundAtom(self._IsCooked, [patty]), + GroundAtom(self._IsCooked, [patty2]), + GroundAtom(self._IsCooked, [patty3]), + GroundAtom(self._IsCooked, [patty4]), + GroundAtom(self._IsCooked, [patty5]) + } + alt_test_goal = { + GroundAtom(self._GoalHack2, [bottom_bun, patty]), + GroundAtom(self._GoalHack2, [bottom_bun2, patty2]), + GroundAtom(self._GoalHack2, [bottom_bun3, patty3]), + GroundAtom(self._GoalHack2, [bottom_bun4, patty4]), + GroundAtom(self._GoalHack2, [bottom_bun5, patty5]) + } + test_task = create_task(state_dict, hidden_state, test_goal, + alt_test_goal) + test_tasks.append(test_task) + + return train_tasks, test_tasks + + if CFG.burger_no_move_task_type == "more_stacks": + train_tasks, test_tasks = create_tasks_for_type_three() + elif CFG.burger_no_move_task_type == "fatter_burger": + train_tasks, test_tasks = create_tasks_for_type_two() + elif CFG.burger_no_move_task_type == "combo_burger": + train_tasks, test_tasks = create_tasks_for_type_one() + else: + raise NotImplementedError( + f"Unrecognized task type: {CFG.burger_no_move_task_type}.") + + if train_or_test == "train": + return train_tasks + return test_tasks + + @property + def predicates(self) -> Set[Predicate]: + return { + self._IsCooked, self._IsSliced, self._HandEmpty, self._Holding, + self._On, self._OnGround, self._Clear, self._GoalHack2, + self._GoalHack3, self._GoalHack4, self._GoalHack5, self._GoalHack6, + self._GoalHack7 + } + + @property + def goal_predicates(self) -> Set[Predicate]: + return {self._On, self._OnGround, self._IsCooked, self._IsSliced} + + @property + def agent_goal_predicates(self) -> Set[Predicate]: + preds_by_task_type = { + "more_stacks": {self._On, self._OnGround, self._GoalHack2}, + "fatter_burger": { + self._On, self._OnGround, self._GoalHack2, self._GoalHack5, + self._GoalHack6, self._GoalHack7 + }, + "combo_burger": { + self._On, self._OnGround, self._GoalHack2, self._GoalHack3, + self._GoalHack4 + } + } + return preds_by_task_type[CFG.burger_no_move_task_type] + + def get_vlm_debug_atom_strs(self, + train_tasks: List[Task]) -> List[List[str]]: + # Convert the default value from List[List[str]] to List[str] to match + # the other entries we'll put into the dictionary. + default = [a[0] for a in super().get_vlm_debug_atom_strs(train_tasks)] + atom_strs_by_task_type = { + "more_stacks": ["Cooked(patty1)"], + "fatter_burger": ["Cooked(patty1)"], + "combo_burger": + ["Cooked(patty1)", "Cut(lettuce1)", "Whole(lettuce1)"] + } + atom_strs_by_task_type = defaultdict(lambda: default, + atom_strs_by_task_type) + atom_strs = atom_strs_by_task_type[CFG.burger_no_move_task_type] + return [[a] for a in atom_strs] diff --git a/predicators/envs/vlm_envs.py b/predicators/envs/vlm_envs.py index a065efa3f5..c5964b7fd6 100644 --- a/predicators/envs/vlm_envs.py +++ b/predicators/envs/vlm_envs.py @@ -131,11 +131,13 @@ def _get_tasks(self, num: int, for _ in range(num) ] - def get_vlm_debug_atom_strs(self, train_tasks: List[Task]) -> Set[str]: + def get_vlm_debug_atom_strs(self, + train_tasks: List[Task]) -> List[List[str]]: del train_tasks - return set([ + atom_strs = set([ "hand_grasping_spoon(hand, spoon)", "hand_grasping_teabag(hand, teabag)", "spoon_in_cup(spoon, cup)", "spoon_on_plate(spoon, plate)", "teabag_in_cup(teabag, cup)", "teabag_on_plate(teabag, plate)" ]) + return [[a] for a in atom_strs] diff --git a/predicators/ground_truth_models/burger/__init__.py b/predicators/ground_truth_models/burger/__init__.py index 8b9dd72f9e..37c7ba1756 100644 --- a/predicators/ground_truth_models/burger/__init__.py +++ b/predicators/ground_truth_models/burger/__init__.py @@ -1,6 +1,12 @@ """Ground truth models for burger environment.""" -from .nsrts import BurgerGroundTruthNSRTFactory -from .options import BurgerGroundTruthOptionFactory +from .nsrts import BurgerGroundTruthNSRTFactory, \ + BurgerNoMoveGroundTruthNSRTFactory +from .options import BurgerGroundTruthOptionFactory, \ + BurgerNoMoveGroundTruthOptionFactory -__all__ = ["BurgerGroundTruthOptionFactory", "BurgerGroundTruthNSRTFactory"] +__all__ = [ + "BurgerGroundTruthOptionFactory", "BurgerGroundTruthNSRTFactory", + "BurgerNoMoveGroundTruthOptionFactory", + "BurgerNoMoveGroundTruthNSRTFactory" +] diff --git a/predicators/ground_truth_models/burger/nsrts.py b/predicators/ground_truth_models/burger/nsrts.py index 408eadb885..b69b476f8c 100644 --- a/predicators/ground_truth_models/burger/nsrts.py +++ b/predicators/ground_truth_models/burger/nsrts.py @@ -21,7 +21,7 @@ def get_nsrts(env_name: str, types: Dict[str, Type], options: Dict[str, ParameterizedOption]) -> Set[NSRT]: # Types - tomato_type = types["tomato"] + tomato_type = types["lettuce"] patty_type = types["patty"] grill_type = types["grill"] @@ -69,7 +69,7 @@ def get_nsrts(env_name: str, types: Dict[str, Type], Pick = options["Pick"] Place = options["Place"] Cook = options["Cook"] - Slice = options["Slice"] + Slice = options["Chop"] nsrts = set() @@ -599,3 +599,168 @@ def get_nsrts(env_name: str, types: Dict[str, Type], nsrts.add(place_nsrt) return nsrts + + +class BurgerNoMoveGroundTruthNSRTFactory(GroundTruthNSRTFactory): + """Ground-truth NSRTs for the Burger environment with no distinct movement + options.""" + + @classmethod + def get_env_names(cls) -> Set[str]: + return {"burger_no_move"} + + @staticmethod + def get_nsrts(env_name: str, types: Dict[str, Type], + predicates: Dict[str, Predicate], + options: Dict[str, ParameterizedOption]) -> Set[NSRT]: + + # Types + tomato_type = types["lettuce"] + patty_type = types["patty"] + + grill_type = types["grill"] + cutting_board_type = types["cutting_board"] + robot_type = types["robot"] + + item_type = types["item"] + object_type = types["object"] + + # Variables + tomato = Variable("?tomato", tomato_type) + patty = Variable("?patty", patty_type) + + grill = Variable("?grill", grill_type) + cutting_board = Variable("?cutting_board", cutting_board_type) + robot = Variable("?robot", robot_type) + + item = Variable("?item", item_type) + obj = Variable("?object", object_type) + + # Predicates + # Adjacent = predicates["Adjacent"] + # AdjacentToNothing = predicates["AdjacentToNothing"] + # Facing = predicates["Facing"] + # OnNothing = predicates["OnNothing"] + + IsCooked = predicates["IsCooked"] + IsSliced = predicates["IsSliced"] + HandEmpty = predicates["HandEmpty"] + Holding = predicates["Holding"] + On = predicates["On"] + OnGround = predicates["OnGround"] + Clear = predicates["Clear"] + + # Options + # Move = options["Move"] + Pick = options["Pick"] + Place = options["Place"] + Cook = options["Cook"] + Slice = options["Chop"] + + nsrts = set() + + # Slice + parameters = [robot, tomato, cutting_board] + option_vars = [robot, tomato, cutting_board] + option = Slice + preconditions = { + LiftedAtom(Clear, [tomato]), + LiftedAtom(On, [tomato, cutting_board]), + LiftedAtom(HandEmpty, [robot]) + } + add_effects = {LiftedAtom(IsSliced, [tomato])} + delete_effects: Set[LiftedAtom] = set() + ignore_effects: Set[Predicate] = set() + slice_nsrt = NSRT("Slice", parameters, preconditions, add_effects, + delete_effects, ignore_effects, option, option_vars, + null_sampler) + nsrts.add(slice_nsrt) + + # Cook + parameters = [robot, patty, grill] + option_vars = [robot, patty, grill] + option = Cook + preconditions = { + LiftedAtom(Clear, [patty]), + LiftedAtom(On, [patty, grill]), + LiftedAtom(HandEmpty, [robot]) + } + add_effects = {LiftedAtom(IsCooked, [patty])} + delete_effects = set() + ignore_effects = set() + cook_nsrt = NSRT("Cook", parameters, preconditions, add_effects, + delete_effects, ignore_effects, option, option_vars, + null_sampler) + nsrts.add(cook_nsrt) + + # PickFromGround + parameters = [robot, item] + option_vars = [robot, item] + option = Pick + preconditions = { + LiftedAtom(Clear, [item]), + # We OnGround over OnNothing here because the latter remains true + # after we pick the object up if it's implemented as Forall-NOT-On, + # and we want it to be deleted after picking it up. + LiftedAtom(OnGround, [item]), + LiftedAtom(HandEmpty, [robot]) + } + add_effects = {LiftedAtom(Holding, [robot, item])} + delete_effects = { + LiftedAtom(Clear, [item]), + LiftedAtom(OnGround, [item]), + LiftedAtom(HandEmpty, [robot]) + } + ignore_effects = set() + pick_nsrt = NSRT("PickFromGround", parameters, preconditions, + add_effects, delete_effects, ignore_effects, option, + option_vars, null_sampler) + nsrts.add(pick_nsrt) + + # Unstack + parameters = [robot, item, obj] + option_vars = [robot, item] + option = Pick + preconditions = { + LiftedAtom(Clear, [item]), + LiftedAtom(On, [item, obj]), + LiftedAtom(HandEmpty, [robot]), + } + add_effects = { + LiftedAtom(Holding, [robot, item]), + LiftedAtom(Clear, [obj]) + } + delete_effects = { + LiftedAtom(Clear, [item]), + LiftedAtom(On, [item, obj]), + LiftedAtom(HandEmpty, [robot]), + } + ignore_effects = set() + unstack_nsrt = NSRT("Unstack", parameters, preconditions, add_effects, + delete_effects, ignore_effects, option, + option_vars, null_sampler) + nsrts.add(unstack_nsrt) + + # Stack + parameters = [robot, item, obj] + option_vars = [robot, item, obj] + option = Place + preconditions = { + LiftedAtom(Holding, [robot, item]), + LiftedAtom(Clear, [obj]) + } + add_effects = { + LiftedAtom(Clear, [item]), + LiftedAtom(On, [item, obj]), + LiftedAtom(HandEmpty, [robot]) + } + delete_effects = { + LiftedAtom(Holding, [robot, item]), + LiftedAtom(Clear, [obj]) + } + stack_nsrt = NSRT("Stack", parameters, preconditions, add_effects, + delete_effects, set(), option, option_vars, + null_sampler) + nsrts.add(stack_nsrt) + + return nsrts diff --git a/predicators/ground_truth_models/burger/options.py b/predicators/ground_truth_models/burger/options.py index 2a3febe6f2..4f450ac323 100644 --- a/predicators/ground_truth_models/burger/options.py +++ b/predicators/ground_truth_models/burger/options.py @@ -1,6 +1,6 @@ """Ground-truth options for the burger environment.""" -from typing import Dict, Iterator, Sequence, Set, Tuple +from typing import Dict, Iterator, Optional, Sequence, Set, Tuple import numpy as np from gym.spaces import Box @@ -28,7 +28,7 @@ def get_options(cls, env_name: str, types: Dict[str, Type], # top_bun_type = types["top_bun"] # bottom_bun_type = types["bottom_bun"] # cheese_type = types["cheese"] - tomato_type = types["tomato"] + tomato_type = types["lettuce"] patty_type = types["patty"] grill_type = types["grill"] @@ -50,8 +50,6 @@ def get_options(cls, env_name: str, types: Dict[str, Type], Holding = predicates["Holding"] On = predicates["On"] - # GoalHack = predicates["GoalHack"] - # Slice def _Slice_terminal(state: State, memory: Dict, objects: Sequence[Object], params: Array) -> bool: @@ -60,7 +58,7 @@ def _Slice_terminal(state: State, memory: Dict, return IsSliced.holds(state, [tomato]) Slice = ParameterizedOption( - "Slice", + "Chop", types=[robot_type, tomato_type, cutting_board_type], params_space=Box(0, 1, (0, )), policy=cls._create_slice_policy(), @@ -148,6 +146,76 @@ def policy(state: State, memory: Dict, objects: Sequence[Object], return policy + @classmethod + def _move_subpolicy(cls, state: State, robot: Object, + to_obj: Object) -> Optional[Action]: + rx, ry = BurgerEnv.get_position(robot, state) + ox, oy = BurgerEnv.get_position(to_obj, state) + + if BurgerEnv.Facing_holds(state, [robot, to_obj]): + return None + + # If we're adjacent to the object but not facing it, turn to face + # it. + if BurgerEnv.Adjacent_holds(state, [robot, to_obj]) and \ + not BurgerEnv.Facing_holds(state, [robot, to_obj]): + if rx == ox: + if ry > oy: + action = Action(np.array([0, 0, 2, 0, 0], + dtype=np.float32)) + elif ry < oy: + action = Action(np.array([0, 0, 0, 0, 0], + dtype=np.float32)) + elif ry == oy: + if rx > ox: + action = Action(np.array([0, 0, 1, 0, 0], + dtype=np.float32)) + elif rx < ox: + action = Action(np.array([0, 0, 3, 0, 0], + dtype=np.float32)) + + else: + # Find the path we need to take to the object. + init = BurgerEnv.get_position(robot, state) + + def _check_goal(s: Tuple[int, int]) -> bool: + sx, sy = s + if BurgerEnv.is_adjacent(sx, sy, ox, oy): + return True + return False + + def _get_successors(s: Tuple[int, int]) -> \ + Iterator[Tuple[None, Tuple[int, int], float]]: + # Find the adjacent cells that are empty. + empty_cells = BurgerEnv.get_empty_cells(state) + sx, sy = s + adjacent_empty = [] + for cell in empty_cells: + cx, cy = cell + if BurgerEnv.is_adjacent(sx, sy, cx, cy): + adjacent_empty.append(cell) + for cell in adjacent_empty: + yield (None, cell, 1.0) + + def heuristic(s: Tuple[int, int]) -> float: + sx, sy = s + return abs(sx - ox) + abs(sy - oy) + + path, _ = utils.run_astar(initial_state=init, + check_goal=_check_goal, + get_successors=_get_successors, + heuristic=heuristic) + + # Now, compute the action to take based on the path we have + # planned. Note that the path is a list of (x, y) tuples + # starting from the location of the robot. + nx, ny = path[1] + dx = np.clip(nx - rx, -1, 1) + dy = np.clip(ny - ry, -1, 1) + action = Action(np.array([dx, dy, -1, 0, 0], dtype=np.float32)) + + return action + @classmethod def _create_move_policy(cls) -> ParameterizedPolicy: @@ -155,68 +223,13 @@ def policy(state: State, memory: Dict, objects: Sequence[Object], params: Array) -> Action: del memory, params # unused robot, to_obj = objects - rx, ry = BurgerEnv.get_position(robot, state) - ox, oy = BurgerEnv.get_position(to_obj, state) - - # If we're adjacent to the object but not facing it, turn to face - # it. - if BurgerEnv.Adjacent_holds(state, [robot, to_obj]) and \ - not BurgerEnv.Facing_holds(state, [robot, to_obj]): - if rx == ox: - if ry > oy: - action = Action( - np.array([0, 0, 2, 0, 0], dtype=np.float32)) - elif ry < oy: - action = Action( - np.array([0, 0, 0, 0, 0], dtype=np.float32)) - elif ry == oy: - if rx > ox: - action = Action( - np.array([0, 0, 1, 0, 0], dtype=np.float32)) - elif rx < ox: - action = Action( - np.array([0, 0, 3, 0, 0], dtype=np.float32)) - - else: - # Find the path we need to take to the object. - init = BurgerEnv.get_position(robot, state) - - def _check_goal(s: Tuple[int, int]) -> bool: - sx, sy = s - if BurgerEnv.is_adjacent(sx, sy, ox, oy): - return True - return False - - def _get_successors(s: Tuple[int, int]) -> \ - Iterator[Tuple[None, Tuple[int, int], float]]: - # Find the adjacent cells that are empty. - empty_cells = BurgerEnv.get_empty_cells(state) - sx, sy = s - adjacent_empty = [] - for cell in empty_cells: - cx, cy = cell - if BurgerEnv.is_adjacent(sx, sy, cx, cy): - adjacent_empty.append(cell) - for cell in adjacent_empty: - yield (None, cell, 1.0) - - def heuristic(s: Tuple[int, int]) -> float: - sx, sy = s - return abs(sx - ox) + abs(sy - oy) - - path, _ = utils.run_astar(initial_state=init, - check_goal=_check_goal, - get_successors=_get_successors, - heuristic=heuristic) - - # Now, compute the action to take based on the path we have - # planned. Note that the path is a list of (x, y) tuples - # starting from the location of the robot. - nx, ny = path[1] - dx = np.clip(nx - rx, -1, 1) - dy = np.clip(ny - ry, -1, 1) - action = Action(np.array([dx, dy, -1, 0, 0], dtype=np.float32)) - + # We put the move policy code in a different function so that + # subclasses can use it. + # This policy will never return None because it only returns None + # when the termination condition is True, and the termination + # condition is checked at each step before this policy is run. + action = cls._move_subpolicy(state, robot, to_obj) + assert action is not None return action return policy @@ -231,3 +244,163 @@ def policy(state: State, memory: Dict, objects: Sequence[Object], return action return policy + + +class BurgerNoMoveGroundTruthOptionFactory(BurgerGroundTruthOptionFactory): + """Ground-truth options for the Burger environment with no distinct + movement options.""" + + @classmethod + def get_env_names(cls) -> Set[str]: + return {"burger_no_move"} + + @classmethod + def get_options(cls, env_name: str, types: Dict[str, Type], + predicates: Dict[str, Predicate], + action_space: Box) -> Set[ParameterizedOption]: + + # Types + # top_bun_type = types["top_bun"] + # bottom_bun_type = types["bottom_bun"] + # cheese_type = types["cheese"] + tomato_type = types["lettuce"] + patty_type = types["patty"] + + grill_type = types["grill"] + cutting_board_type = types["cutting_board"] + robot_type = types["robot"] + + item_type = types["item"] + # station_type = types["station"] + object_type = types["object"] + + # Predicates + # Adjacent = predicates["Adjacent"] + # AdjacentToNothing = predicates["AdjacentToNothing"] + # Facing = predicates["Facing"] + # AdjacentNotFacing = predicates["AdjacentNotFacing"] + IsCooked = predicates["IsCooked"] + IsSliced = predicates["IsSliced"] + HandEmpty = predicates["HandEmpty"] + Holding = predicates["Holding"] + On = predicates["On"] + + # Slice + def _Slice_terminal(state: State, memory: Dict, + objects: Sequence[Object], params: Array) -> bool: + del memory, params # unused + _, tomato, _ = objects + return IsSliced.holds(state, [tomato]) + + Slice = ParameterizedOption( + "Chop", + types=[robot_type, tomato_type, cutting_board_type], + params_space=Box(0, 1, (0, )), + policy=cls._create_slice_policy(), + initiable=lambda s, m, o, p: True, + terminal=_Slice_terminal) + + # Cook + def _Cook_terminal(state: State, memory: Dict, + objects: Sequence[Object], params: Array) -> bool: + del memory, params # unused + _, patty, _ = objects + return IsCooked.holds(state, [patty]) + + Cook = ParameterizedOption("Cook", + types=[robot_type, patty_type, grill_type], + params_space=Box(0, 1, (0, )), + policy=cls._create_cook_policy(), + initiable=lambda s, m, o, p: True, + terminal=_Cook_terminal) + + # Pick + def _Pick_terminal(state: State, memory: Dict, + objects: Sequence[Object], params: Array) -> bool: + del memory, params # unused + robot, item = objects + return Holding.holds(state, [robot, item]) + + Pick = ParameterizedOption("Pick", + types=[robot_type, item_type], + params_space=Box(0, 1, (0, )), + policy=cls._create_pick_policy(), + initiable=lambda s, m, o, p: True, + terminal=_Pick_terminal) + + # Place + def _Place_terminal(state: State, memory: Dict, + objects: Sequence[Object], params: Array) -> bool: + del memory, params # unused + robot, item, obj = objects + return HandEmpty.holds(state, [robot]) and On.holds( + state, [item, obj]) + + Place = ParameterizedOption("Place", + types=[robot_type, item_type, object_type], + params_space=Box(0, 1, (0, )), + policy=cls._create_place_policy(), + initiable=lambda s, m, o, p: True, + terminal=_Place_terminal) + + return {Pick, Place, Cook, Slice} + + @classmethod + def _create_slice_policy(cls) -> ParameterizedPolicy: + + def policy(state: State, memory: Dict, objects: Sequence[Object], + params: Array) -> Action: + del memory, params # unused + robot, item, _ = objects + move_action = cls._move_subpolicy(state, robot, item) + slice_action = Action(np.array([0, 0, -1, 1, 0], dtype=np.float32)) + if move_action is not None: + return move_action + return slice_action + + return policy + + @classmethod + def _create_cook_policy(cls) -> ParameterizedPolicy: + + def policy(state: State, memory: Dict, objects: Sequence[Object], + params: Array) -> Action: + del memory, params # unused + robot, item, _ = objects + move_action = cls._move_subpolicy(state, robot, item) + cook_action = Action(np.array([0, 0, -1, 1, 0], dtype=np.float32)) + if move_action is not None: + return move_action + return cook_action + + return policy + + @classmethod + def _create_pick_policy(cls) -> ParameterizedPolicy: + + def policy(state: State, memory: Dict, objects: Sequence[Object], + params: Array) -> Action: + del memory, params # unused + robot, item = objects + move_action = cls._move_subpolicy(state, robot, item) + pick_action = Action(np.array([0, 0, -1, 0, 1], dtype=np.float32)) + if move_action is not None: + return move_action + return pick_action + + return policy + + @classmethod + def _create_place_policy(cls) -> ParameterizedPolicy: + + def policy(state: State, memory: Dict, objects: Sequence[Object], + params: Array) -> Action: + del memory, params # unused + robot, _, to_obj = objects + move_action = cls._move_subpolicy(state, robot, to_obj) + place_action = Action(np.array([0, 0, -1, 0, 1], dtype=np.float32)) + if move_action is not None: + return move_action + return place_action + + return policy diff --git a/predicators/nsrt_learning/strips_learning/base_strips_learner.py b/predicators/nsrt_learning/strips_learning/base_strips_learner.py index 5258273396..5d3aa998ac 100644 --- a/predicators/nsrt_learning/strips_learning/base_strips_learner.py +++ b/predicators/nsrt_learning/strips_learning/base_strips_learner.py @@ -2,6 +2,7 @@ import abc import logging +from collections import defaultdict from typing import Any, Dict, List, Optional, Set, Tuple from predicators import utils @@ -315,6 +316,46 @@ def _induce_preconditions_via_intersection(pnad: PNAD) -> Set[LiftedAtom]: preconditions = lifted_atoms else: preconditions &= lifted_atoms + + return preconditions + + @staticmethod + def _induce_preconditions_via_soft_intersection( + pnad: PNAD) -> Set[LiftedAtom]: + """Given a PNAD with a nonempty datastore, compute the preconditions + for the PNAD's operator from a soft intersection of all lifted + preimages. + + Keep the preconditions that appear in a more than a certain percentage + of segments in the PNAD's datastore, as determined by + CFG.precondition_soft_intersection_threshold_percent. + """ + assert len(pnad.datastore) > 0 + threshold_count = int( + len(pnad.datastore) * + CFG.precondition_soft_intersection_threshold_percent) + lifted_atom_counts: dict[LiftedAtom, int] = defaultdict(int) + + for segment, var_to_obj in pnad.datastore: + objects = set(var_to_obj.values()) + obj_to_var = {o: v for v, o in var_to_obj.items()} + atoms = { + atom + for atom in segment.init_atoms + if all(o in objects for o in atom.objects) + } + lifted_atoms = {atom.lift(obj_to_var) for atom in atoms} + + for la in lifted_atoms: + lifted_atom_counts[la] += 1 + + # Keep the lifted atoms that appear as preconditions in more than + # threshold_count of the segments. + preconditions = { + la + for la, count in lifted_atom_counts.items() + if count > threshold_count + } return preconditions @staticmethod diff --git a/predicators/nsrt_learning/strips_learning/clustering_learner.py b/predicators/nsrt_learning/strips_learning/clustering_learner.py index fb0eae8dbe..7cbb9c6b6c 100644 --- a/predicators/nsrt_learning/strips_learning/clustering_learner.py +++ b/predicators/nsrt_learning/strips_learning/clustering_learner.py @@ -116,7 +116,12 @@ class ClusterAndIntersectSTRIPSLearner(ClusteringSTRIPSLearner): def _learn_pnad_preconditions(self, pnads: List[PNAD]) -> List[PNAD]: new_pnads = [] for pnad in pnads: - preconditions = self._induce_preconditions_via_intersection(pnad) + if CFG.cluster_and_intersect_soft_intersection_for_preconditions: + preconditions = \ + self._induce_preconditions_via_soft_intersection(pnad) + else: + preconditions = self._induce_preconditions_via_intersection( + pnad) # Since we are taking an intersection, we're guaranteed that the # datastore can't change, so we can safely use pnad.datastore here. new_pnads.append( diff --git a/predicators/settings.py b/predicators/settings.py index c3e9dfe57b..4dc482e377 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -356,8 +356,12 @@ class GlobalSettings: # burger env parameters burger_render_set_of_marks = True - gridworld_num_rows = 5 - gridworld_num_cols = 5 + # Which type of train/test tasks to generate. Options are "more_stacks", + # "fatter_burger", "combo_burger". + burger_no_move_task_type = "more_stacks" + # Replace actual rendering with dummy rendering (black 16x16 image) to speed + # up rendering -- used in testing or when debugging. + burger_dummy_render = False # parameters for random options approach random_options_max_tries = 100 @@ -420,6 +424,8 @@ class GlobalSettings: vlm_model_name = "gemini-pro-vision" vlm_temperature = 0.0 vlm_num_completions = 1 + vlm_include_cropped_images = False + use_hardcoded_vlm_atom_proposals = False # parameters for the vlm_open_loop planning approach vlm_open_loop_use_training_demos = False @@ -488,6 +494,7 @@ class GlobalSettings: strips_learner = "cluster_and_intersect" disable_harmlessness_check = False # some methods may want this to be True enable_harmless_op_pruning = False # some methods may want this to be True + precondition_soft_intersection_threshold_percent = 0.8 # between 0 and 1 backchaining_check_intermediate_harmlessness = False pnad_search_without_del = False pnad_search_timeout = 10.0 @@ -506,6 +513,7 @@ class GlobalSettings: # associated with their PNAD in order to not be pruned during operator # learning. cluster_and_intersect_min_datastore_fraction = 0.0 + cluster_and_intersect_soft_intersection_for_preconditions = False # torch GPU usage setting use_torch_gpu = False @@ -684,6 +692,7 @@ class GlobalSettings: grammar_search_vlm_atom_label_prompt_type = "per_scene_naive" grammar_search_vlm_atom_proposal_use_debug = False grammar_search_parallelize_vlm_labeling = True + grammar_search_select_all_debug = False # grammar search clustering algorithm parameters grammar_search_clustering_gmm_num_components = 10 diff --git a/predicators/structs.py b/predicators/structs.py index a27c20947b..a9daed57e3 100644 --- a/predicators/structs.py +++ b/predicators/structs.py @@ -1238,6 +1238,7 @@ class ImageOptionTrajectory: """ _objects: Collection[Object] _state_imgs: List[List[PIL.Image.Image]] + _cropped_state_imgs: List[List[PIL.Image.Image]] _actions: List[_Option] _states: Optional[List[State]] = field(default=None) _is_demo: bool = field(default=False) @@ -1255,6 +1256,11 @@ def imgs(self) -> List[List[PIL.Image.Image]]: """State images in the trajectory.""" return self._state_imgs + @property + def cropped_imgs(self) -> List[List[PIL.Image.Image]]: + """Cropped versions of state images in the trajectory.""" + return self._cropped_state_imgs + @property def objects(self) -> Collection[Object]: """Objects important to the trajectory.""" diff --git a/tests/approaches/test_grammar_search_invention_approach.py b/tests/approaches/test_grammar_search_invention_approach.py index e33e4815b3..14e1e96e27 100644 --- a/tests/approaches/test_grammar_search_invention_approach.py +++ b/tests/approaches/test_grammar_search_invention_approach.py @@ -233,18 +233,15 @@ def test_no_select_invention(): def test_geo_and_vlm_invention(): """Test constructing an atom dataset with both geo and vlm predicates.""" utils.reset_config({ - "env": - "ice_tea_making", - "num_train_tasks": - 1, - "num_test_tasks": - 0, - "offline_data_method": - "geo_and_demo+labelled_atoms", - "data_dir": - "tests/datasets/mock_vlm_datasets", + "env": "ice_tea_making", + "num_train_tasks": 1, + "num_test_tasks": 0, + "offline_data_method": "geo_and_demo+labelled_atoms", + "data_dir": "tests/datasets/mock_vlm_datasets", "handmade_demo_filename": - "ice_tea_making__demo+labelled_atoms__manual__1.txt" + "ice_tea_making__demo+labelled_atoms__manual__1.txt", + "grammar_search_select_all_debug": True, + "grammar_search_use_handcoded_debug_grammar": True }) env = IceTeaMakingEnv() train_tasks = env.get_train_tasks() @@ -270,6 +267,36 @@ def test_geo_and_vlm_invention(): assert approach._get_current_predicates() == env.goal_predicates # pylint:disable=protected-access +def test_select_all_debug_predicates(): + """Test selecting all debug predicates from the debug grammar.""" + utils.reset_config({ + "env": "ice_tea_making", + "num_train_tasks": 1, + "num_test_tasks": 0, + "offline_data_method": "geo_and_demo+labelled_atoms", + "data_dir": "tests/datasets/mock_vlm_datasets", + "handmade_demo_filename": + "ice_tea_making__demo+labelled_atoms__manual__1.txt", + "grammar_search_select_all_debug": True, + "grammar_search_use_handcoded_debug_grammar": True + }) + env = IceTeaMakingEnv() + train_tasks = env.get_train_tasks() + predicates, _ = utils.parse_config_excluded_predicates(env) + loaded_dataset = create_dataset(env, train_tasks, + get_gt_options(env.get_name()), predicates) + approach = GrammarSearchInventionApproach(env.goal_predicates, + get_gt_options(env.get_name()), + env.types, env.action_space, + train_tasks) + approach.learn_from_offline_dataset(loaded_dataset) + # Because we have not listed any debug predicates for this environment, + # no predicates will be selected. + # A better test would test this on some environment that actually has both + # geometric and VLM debug predicates. + assert len(approach._learned_predicates) == 0 # pylint:disable=protected-access + + def test_euclidean_grammar(): """Tests for the EuclideanGrammar.""" utils.reset_config({"env": "stick_button_move"}) diff --git a/tests/datasets/test_datasets.py b/tests/datasets/test_datasets.py index 65d2bbe74c..447898d23f 100644 --- a/tests/datasets/test_datasets.py +++ b/tests/datasets/test_datasets.py @@ -823,3 +823,60 @@ def test_create_ground_atom_data_from_generated_demos(config): vlm_dataset = create_ground_atom_data_from_generated_demos( dataset, env, predicates, train_tasks, vlm) assert len(vlm_dataset.annotations) == 1 + + +def test_vlm_include_cropped_images(): + """Tests creating a ground atom data with cropped images.""" + utils.reset_config({ + "env": "cover", + "approach": "oracle", + "offline_data_method": "demo", + "offline_data_planning_timeout": 500, + "option_learner": "no_learning", + "num_train_tasks": 1, + "included_options": "PickPlace", + "excluded_predicates": "all", + "vlm_include_cropped_images": True + }) + env = CoverEnv() + train_tasks = [t.task for t in env.get_train_tasks()] + predicates, _ = utils.parse_config_excluded_predicates(env) + options = parse_config_included_options(env) + dataset = create_dataset(env, train_tasks, options, predicates) + for state in dataset.trajectories[0].states: + state.simulator_state = {} + state.simulator_state["images"] = [np.zeros((32, 32), dtype=np.uint8)] + vlm = _DummyVLM() + with pytest.raises(NotImplementedError) as e: + _ = create_ground_atom_data_from_generated_demos( + dataset, env, predicates, train_tasks, vlm) + assert "Cropped images not implemented for cover." in str(e) + + utils.reset_config({ + "env": "ice_tea_making", + "num_train_tasks": 1, + "offline_data_method": "saved_vlm_img_demos_folder", + "data_dir": "tests/datasets/mock_vlm_datasets", + "seed": 456, + "vlm_trajs_folder_name": "ice_tea_making__vlm_demos__456__1", + "grammar_search_vlm_atom_proposal_prompt_type": + "options_labels_whole_traj", + "grammar_search_vlm_atom_label_prompt_type": + "img_option_diffs_label_history", + "pretrained_model_prompt_cache_dir": + "tests/datasets/mock_vlm_datasets/cache", + "vlm_include_cropped_images": True + }) + env = IceTeaMakingEnv() + train_tasks = env.get_train_tasks() + predicates, _ = utils.parse_config_excluded_predicates(env) + vlm = _DummyVLM() + with pytest.raises(NotImplementedError) as e: + _ = create_ground_atom_data_from_saved_img_trajs( + env, train_tasks, predicates, get_gt_options(env.get_name()), vlm) + assert "Cropped images not implemented for ice_tea_making." in str(e) + for dirpath, _, filenames in os.walk( + CFG.pretrained_model_prompt_cache_dir): + # Remove regular files, ignore directories + for filename in filenames: + os.unlink(os.path.join(dirpath, filename)) diff --git a/tests/envs/test_burger.py b/tests/envs/test_burger.py index 97b438410d..01400e4bcd 100644 --- a/tests/envs/test_burger.py +++ b/tests/envs/test_burger.py @@ -6,7 +6,7 @@ import pytest from predicators import utils -from predicators.envs.burger import BurgerEnv +from predicators.envs.burger import BurgerEnv, BurgerNoMoveEnv from predicators.ground_truth_models import get_gt_nsrts, get_gt_options from predicators.settings import CFG from predicators.structs import Action, GroundAtom @@ -84,7 +84,7 @@ def test_burger(): grill = [obj for obj in task.init if obj.name == "grill"][0] patty = [obj for obj in task.init if obj.name == "patty"][0] robot = [obj for obj in task.init if obj.name == "robot"][0] - tomato = [obj for obj in task.init if obj.name == "tomato"][0] + tomato = [obj for obj in task.init if obj.name == "lettuce"][0] cutting_board = [obj for obj in task.init if obj.name == "cutting_board"][0] cheese = [obj for obj in task.init if obj.name == "cheese"][0] @@ -177,6 +177,12 @@ def test_burger(): ]) in utils.abstract(traj.states[-1], env.predicates) + # Test _Clear_holds -- that a held object is not clear. + Clear = [p for p in env.predicates if p.name == "Clear"][0] + assert GroundAtom(Clear, + [patty]) not in utils.abstract(traj.states[9], + env.predicates) + # Test get_cell_in_direction x, y = env.get_cell_in_direction(1, 1, "left") assert x == 0 and y == 1 @@ -208,6 +214,10 @@ def test_burger(): env.render_state_plt(traj.states[0], task) env.render_state_plt(traj.states[5], task) env.render_state_plt(traj.states[-1], task) + # Test labeling when a cutting board is not in the state. + no_cutting_board_state = traj.states[12].copy() + del no_cutting_board_state.data[env._cutting_board] # pylint: disable=protected-access + env.render_state_plt(no_cutting_board_state, task) # Test interface for collecting demonstrations event_to_action = env.get_event_to_action_fn() @@ -275,3 +285,176 @@ def test_burger(): # purposes. for _ in range(50): _ = env.get_edge_cells_for_object_placement(rng) + + +def test_burger_no_move(): + """Tests for BurgerNoMoveEnv.""" + + utils.reset_config({ + "env": "burger_no_move", + "option_model_terminate_on_repeat": False, + "sesame_max_skeletons_optimized": 1000, + "sesame_max_samples_per_step": 1, + "sesame_task_planner": "fdopt", + "burger_no_move_task_type": "combo_burger", + "burger_dummy_render": True, + "seed": 0 + }) + env = BurgerNoMoveEnv() + task = env.get_test_tasks()[0] + assert len(env.predicates) == 13 + assert len(env.goal_predicates) == 4 + assert len(env.agent_goal_predicates) == 5 + assert len(env.get_vlm_debug_atom_strs([])) == 3 + rng = np.random.default_rng(0) + assert len(env.get_edge_cells_for_object_placement( + rng)) == (env.num_cols - 2) * 2 + (env.num_rows - 2) * 2 + + # Test _OnGround_holds + s = task.init + patty = [o for o in s if o.name == "patty1"][0] + OnGround = [p for p in env.predicates if p.name == "OnGround"][0] + assert GroundAtom(OnGround, [patty]) in utils.abstract(s, env.predicates) + + # Test the GoalHacks, but not that rigorously, because we will likely + # change their definitions. + abstract_state = utils.abstract(s, env.predicates) + goalhacks_to_check = ["GoalHack2", "GoalHack3", "GoalHack4", "GoalHack5"] + for atom in abstract_state: + assert atom.predicate.name not in goalhacks_to_check + + assert env.get_name() == "burger_no_move" + options = get_gt_options(env.get_name()) + assert len(options) == 4 + nsrts = get_gt_nsrts(env.get_name(), env.predicates, options) + assert len(nsrts) == 5 + + Cook = [n for n in nsrts if n.name == "Cook"][0] + Slice = [n for n in nsrts if n.name == "Slice"][0] + PickFromGround = [n for n in nsrts if n.name == "PickFromGround"][0] + Unstack = [n for n in nsrts if n.name == "Unstack"][0] + Stack = [n for n in nsrts if n.name == "Stack"][0] + + grill = [obj for obj in s if obj.name == "grill"][0] + patty = [obj for obj in s if obj.name == "patty1"][0] + robot = [obj for obj in s if obj.name == "robot"][0] + lettuce = [obj for obj in s if obj.name == "lettuce1"][0] + cutting_board = [obj for obj in s if obj.name == "cutting_board"][0] + top_bun = [obj for obj in s if obj.name == "top_bun1"][0] + bottom_bun = [obj for obj in s if obj.name == "bottom_bun1"][0] + + plan = [ + PickFromGround.ground((robot, patty)), + Stack.ground((robot, patty, grill)), + Cook.ground((robot, patty, grill)), + Unstack.ground((robot, patty, grill)), + Stack.ground((robot, patty, bottom_bun)), + PickFromGround.ground((robot, lettuce)), + Stack.ground((robot, lettuce, cutting_board)), + Slice.ground((robot, lettuce, cutting_board)), + Unstack.ground((robot, lettuce, cutting_board)), + Stack.ground((robot, lettuce, patty)), + PickFromGround.ground((robot, top_bun)), + Stack.ground((robot, top_bun, lettuce)) + ] + + option_plan = [n.option.ground(n.option_objs, []) for n in plan] + policy = utils.option_plan_to_policy(option_plan) + traj, _ = utils.run_policy(policy, + env, + "test", + 0, + termination_function=lambda s: False, + max_num_steps=CFG.horizon, + exceptions_to_break_on={ + utils.OptionExecutionFailure, + utils.HumanDemonstrationFailure, + }, + monitor=None) + assert task.task.goal_holds(traj.states[-1]) + + # Coverage for move part of slice policy. + # Put the lettuce on the cutting board and have the robot move to it when + # slicing. + s.set(lettuce, "row", 0) + s.set(lettuce, "col", 3) + s.set(lettuce, "z", 1) + plan = [Slice.ground((robot, lettuce, cutting_board))] + option_plan = [n.option.ground(n.option_objs, []) for n in plan] + policy = utils.option_plan_to_policy(option_plan) + traj, _ = utils.run_policy(policy, + env, + "test", + 0, + termination_function=lambda s: False, + max_num_steps=CFG.horizon, + exceptions_to_break_on={ + utils.OptionExecutionFailure, + utils.HumanDemonstrationFailure, + }, + monitor=None) + assert traj.states[-1].get(robot, "row") == 1 + assert traj.states[-1].get(robot, "col") == 3 + + # Coverage for move part of cook policy. + # Put the patty on the grill and have the robot move to it when cooking. + s.set(patty, "row", 0) + s.set(patty, "col", 2) + s.set(patty, "z", 1) + plan = [Cook.ground((robot, patty, grill))] + option_plan = [n.option.ground(n.option_objs, []) for n in plan] + policy = utils.option_plan_to_policy(option_plan) + traj, _ = utils.run_policy(policy, + env, + "test", + 0, + termination_function=lambda s: False, + max_num_steps=CFG.horizon, + exceptions_to_break_on={ + utils.OptionExecutionFailure, + utils.HumanDemonstrationFailure, + }, + monitor=None) + assert traj.states[-1].get(robot, "row") == 1 + assert traj.states[-1].get(robot, "col") == 2 + + utils.reset_config({ + "env": "burger_no_move", + "option_model_terminate_on_repeat": False, + "sesame_max_skeletons_optimized": 1000, + "sesame_max_samples_per_step": 1, + "sesame_task_planner": "fdopt", + "burger_no_move_task_type": "more_stacks", + "burger_dummy_render": True, + "seed": 0 + }) + env = BurgerNoMoveEnv() + task = env.get_test_tasks()[0] + + utils.reset_config({ + "env": "burger_no_move", + "option_model_terminate_on_repeat": False, + "sesame_max_skeletons_optimized": 1000, + "sesame_max_samples_per_step": 1, + "sesame_task_planner": "fdopt", + "burger_no_move_task_type": "fatter_burger", + "burger_dummy_render": True, + "seed": 0 + }) + env = BurgerNoMoveEnv() + task = env.get_train_tasks()[0] + + utils.reset_config({ + "env": "burger_no_move", + "option_model_terminate_on_repeat": False, + "sesame_max_skeletons_optimized": 1000, + "sesame_max_samples_per_step": 1, + "sesame_task_planner": "fdopt", + "burger_no_move_task_type": "fake", + "burger_dummy_render": True, + "seed": 0 + }) + env = BurgerNoMoveEnv() + with pytest.raises(NotImplementedError) as e: + task = env.get_test_tasks()[0] + assert "Unrecognized task type: fake." in str(e) diff --git a/tests/envs/test_cover.py b/tests/envs/test_cover.py index 575f6b4b22..44f3695ccc 100644 --- a/tests/envs/test_cover.py +++ b/tests/envs/test_cover.py @@ -780,5 +780,6 @@ def test_debug_atoms_vlm_str(): 'Holding(block1)', 'HandEmpty()', 'IsTarget(target1)', 'IsBlock(block0)' ] + gt_debug_strs = [[a] for a in gt_debug_strs] for debug_str in gt_debug_strs: assert debug_str in debug_atoms_strs diff --git a/tests/nsrt_learning/strips_learning/test_clustering_learner.py b/tests/nsrt_learning/strips_learning/test_clustering_learner.py index dcdb3f350b..a6aee2961b 100644 --- a/tests/nsrt_learning/strips_learning/test_clustering_learner.py +++ b/tests/nsrt_learning/strips_learning/test_clustering_learner.py @@ -253,6 +253,51 @@ def _simulate(s, a): annotations=None) assert len(pnads) == 0 + # Test cluster and intersect soft intersection. + utils.reset_config({ + "strips_learner": + "cluster_and_intersect", + "cluster_and_intersect_soft_intersection_for_preconditions": + True, + "precondition_soft_intersection_threshold_percent": + 0.6 + }) + s4 = State({obj: [0.0, 1.0, 0.0, 0.0, 0.0]}) + a4 = Action([], Interact) + ns4 = State({obj: [0.0, 1.0, 0.0, 1.0, 0.0]}) + g4 = {IsHappy([obj])} + traj4 = LowLevelTrajectory([s4, ns4], [a4], True, 1) + task4 = Task(s4, g4) + segment4 = Segment(traj4, utils.abstract(s4, preds), + utils.abstract(ns4, preds), Interact) + pnads = learn_strips_operators( + [traj1, traj2, traj3, traj4], [task1, task2, task3, task4], + preds, [[segment1], [segment2], [segment3], [segment4]], + verify_harmlessness=False, + annotations=None) + assert len(pnads) == 2 + assert len(pnads[0].op.preconditions) == 1 + # When we take atoms that appear >= 60% of the time as preconditions, + # IsGreen will show up, because it occurs 2/3 times. + assert list(pnads[0].op.preconditions)[0].predicate == IsGreen + + utils.reset_config({ + "strips_learner": + "cluster_and_intersect", + "cluster_and_intersect_soft_intersection_for_preconditions": + True, + "precondition_soft_intersection_threshold_percent": + 0.7 + }) + pnads = learn_strips_operators( + [traj1, traj2, traj3, traj4], [task1, task2, task3, task4], + preds, [[segment1], [segment2], [segment3], [segment4]], + verify_harmlessness=False, + annotations=None) + assert len(pnads) == 2 + # Now that the threshold is 70%, there will be no chosen preconditions. + assert len(pnads[0].op.preconditions) == 0 + def test_cluster_and_search_strips_learner(): """Tests for ClusterAndSearchSTRIPSLearner.""" diff --git a/tests/test_structs.py b/tests/test_structs.py index 9dc2084b56..efd45d20d9 100644 --- a/tests/test_structs.py +++ b/tests/test_structs.py @@ -7,10 +7,11 @@ from predicators import utils from predicators.structs import NSRT, PNAD, Action, DefaultState, \ DemonstrationQuery, DummyOption, EnvironmentTask, GroundAtom, \ - GroundMacro, InteractionRequest, InteractionResult, LDLRule, LiftedAtom, \ - LiftedDecisionList, LowLevelTrajectory, Macro, Object, \ - ParameterizedOption, Predicate, Query, Segment, State, STRIPSOperator, \ - Task, Type, Variable, _Atom, _GroundNSRT, _GroundSTRIPSOperator, _Option + GroundMacro, ImageOptionTrajectory, InteractionRequest, \ + InteractionResult, LDLRule, LiftedAtom, LiftedDecisionList, \ + LowLevelTrajectory, Macro, Object, ParameterizedOption, Predicate, Query, \ + Segment, State, STRIPSOperator, Task, Type, Variable, _Atom, _GroundNSRT, \ + _GroundSTRIPSOperator, _Option def test_object_type(): @@ -792,6 +793,33 @@ def test_low_level_trajectory(): traj = LowLevelTrajectory(states[:-1], actions) +def test_image_option_trajectory(): + """Tests for the ImageOptionTrajectory class.""" + # This setup is copied from the test for the LowLevelTrajectory class. + cup_type = Type("cup_type", ["feat1"]) + plate_type = Type("plate_type", ["feat1", "feat2"]) + cup = cup_type("cup") + plate = plate_type("plate") + state0 = State({cup: [0.5], plate: [1.0, 1.2]}) + state1 = State({cup: [0.5], plate: [1.1, 1.2]}) + state2 = State({cup: [0.8], plate: [1.5, 1.2]}) + states = [state0, state1, state2] + action0 = Action([0.4]) + action1 = Action([0.6]) + actions = [action0, action1] + + def make_img(): + return np.zeros((32, 32), dtype=np.uint8) + + state_imgs = [[make_img(), make_img()] for i in range(3)] + cropped_state_imgs = [[make_img(), make_img()] for i in range(3)] + objs = set(state0) + img_option_traj = ImageOptionTrajectory(objs, state_imgs, + cropped_state_imgs, actions, + states, True, 0) + assert len(img_option_traj.cropped_imgs) == 3 + + def test_segment(): """Tests for Segment class.""" cup_type = Type("cup_type", ["feat1"]) From 8b9420e61488ca7aa06adf68a1b275cbcb0ed9c2 Mon Sep 17 00:00:00 2001 From: NishanthJKumar Date: Thu, 22 Aug 2024 12:00:00 -0700 Subject: [PATCH 38/38] try updating codeowners to see if checks now run --- .github/CODEOWNERS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 29ec3570f0..58c6725365 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,2 +1,2 @@ # Default owners for all files. - @NishanthJKumar @wmcclinton \ No newline at end of file + @NishanthJKumar \ No newline at end of file

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