still broken

predicators/behavior_utils/behavior_utils.py

@@ -1076,7 +1076,7 @@ def sample_navigation_params(igibson_behavior_env: "BehaviorEnv",
     Implemented in a separate method to enable code reuse in
     option_model_fns.
     """
-    closeness_limit = 2.00
+    closeness_limit = 1.00 #2.00
     nearness_limit = 0.15
     distance = nearness_limit + (
         (closeness_limit - nearness_limit) * rng.random())

predicators/behavior_utils/motion_planner_fns.py

@@ -135,6 +135,7 @@ def sample_fn(env: "BehaviorEnv",
             obstacles=obstacles,
             override_sample_fn=lambda: sample_fn(env, rng),
             rng=rng,
+            max_distance=0.01,
         )
         p.restoreState(state)
     else:

predicators/behavior_utils/option_model_fns.py

@@ -123,7 +123,11 @@ def graspObjectOptionModel(_state: State, env: "BehaviorEnv") -> None:
             rh_final_grasp_postion,
             p.getQuaternionFromEuler(rh_final_grasp_orn))
 
-        # 3. Close hand and simulate grasp
+        # 1.1 step the environment a few timesteps to update location.
+        for _ in range(3):
+            env.step(np.zeros(env.action_space.shape))
+
+        # 2. Close hand and simulate grasp.
         a = np.zeros(env.action_space.shape, dtype=float)
         a[16] = 1.0
         assisted_grasp_action = np.zeros(28, dtype=float)
@@ -141,7 +145,7 @@ def graspObjectOptionModel(_state: State, env: "BehaviorEnv") -> None:
         env.robots[0].parts["right_hand"].handle_assisted_grasping(
             assisted_grasp_action,
             override_ag_data=(grasp_obj_body_id, -1),
-            bypass_force_check=True)
+            bypass_force_check=False)
         # 3.2 step the environment a few timesteps to complete grasp
         for _ in range(5):
             env.step(a)
@@ -216,7 +220,7 @@ def placeOntopObjectOptionModel(_init_state: State,
             rh_orig_grasp_position, rh_orig_grasp_orn)
         # this is running a series of zero action to step simulator
         # to let the object fall into its place
-        for _ in range(15):
+        for _ in range(25):
             env.step(np.zeros(env.action_space.shape))
         # Check whether object is ontop of not a target object
         objs_under = set()

predicators/envs/behavior.py

@@ -775,7 +775,7 @@ def _reachable_classifier(self,
             return False
         return (np.linalg.norm(  # type: ignore
             np.array(robot_obj.get_position()) -
-            np.array(ig_obj.get_position())) < 2)
+            np.array(ig_obj.get_position())) < 1.0)
 
     def _reachable_nothing_classifier(
             self,

predicators/main.py

@@ -34,6 +34,8 @@
         --seed 0 --excluded_predicates all
 """
 
+import curses
+import numpy as np
 import logging
 import os
 import sys
@@ -51,7 +53,7 @@
     BilevelPlanningApproach
 from predicators.approaches.gnn_approach import GNNApproach
 from predicators.datasets import create_dataset
-from predicators.envs import BaseEnv, create_new_env
+from predicators.envs import BaseEnv, create_new_env, get_or_create_env
 from predicators.envs.behavior import BehaviorEnv
 from predicators.planning import _run_plan_with_option_model
 from predicators.settings import CFG
@@ -340,6 +342,23 @@ def _run_testing(env: BaseEnv, approach: BaseApproach) -> Metrics:
                 last_plan = approach.get_last_plan()
                 last_traj = approach.get_last_traj()
                 option_model_start_time = time.time()
+                if CFG.env == "behavior" and \
+                    CFG.behavior_mode == 'iggui':  # pragma: no cover
+                    env = get_or_create_env('behavior')
+                    assert isinstance(env, BehaviorEnv)
+                    win = curses.initscr()
+                    win.nodelay(True)
+                    win.addstr(
+                        0, 0,
+                        "VIDEO CREATION MODE: You have time to position the iggui window \
+                        to the location you want for recording. Type 'q' to indicate you \
+                        have finished positioning: ")
+                    flag = win.getch()
+                    while flag == -1 or chr(flag) != 'q':
+                        env.igibson_behavior_env.step(np.zeros(env.action_space.shape))
+                        flag = win.getch()
+                    curses.endwin()
+                    logging.info("VIDEO CREATION MODE: Starting planning.")
                 traj, solved = _run_plan_with_option_model(
                     task, test_task_idx, approach.get_option_model(),
                     last_plan, last_traj)

predicators/planning.py

@@ -687,6 +687,7 @@ def _run_plan_with_option_model(
             lambda s, m, o, p: True).ground(option.objects, option.params)
         action_option.memory = option.memory
         actions[idx].set_option(action_option)
+        time.sleep(2)
     # Since we're not checking the expected_atoms, we need to
     # explicitly check if the goal is achieved.
     if task.goal_holds(traj[-1]):
@@ -1011,7 +1012,7 @@ def _sesame_plan_with_fast_downward(
     potentially add effects to null operators, but this ability is not
     implemented here.
     """
-    init_atoms = utils.abstract(task.init, predicates)
+    init_atoms = utils.abstract(task.init, predicates, skip_allclose_check=True)
     objects = list(task.init)
     start_time = time.perf_counter()
     # Create the domain and problem strings, then write them to tempfiles.

predicators/utils.py

@@ -2542,7 +2542,10 @@ class VideoMonitor(Monitor):
 
     def observe(self, state: State, action: Optional[Action]) -> None:
         del state  # unused
-        self._video.extend(self._render_fn(action, None))
+        render_state = self._render_fn(action, None)
+        self._video.extend(render_state)
+        self._video.extend(render_state)
+        self._video.extend(render_state)
 
     def get_video(self) -> Video:
         """Return the video."""