diff --git a/predicators/spot_utils/mock_env/mock_env_creator_base.py b/predicators/spot_utils/mock_env/mock_env_creator_base.py
index fcfc160d8..6f2505e12 100644
--- a/predicators/spot_utils/mock_env/mock_env_creator_base.py
+++ b/predicators/spot_utils/mock_env/mock_env_creator_base.py
@@ -5,6 +5,7 @@
 - RGB-D observations with object detections
 - Task-specific configurations
 - Planning and visualization utilities
+- Support for belief space planning (when enabled in environment)
 
 The environment data is stored in a directory specified by CFG.mock_env_data_dir.
 This includes:
@@ -12,8 +13,6 @@
 - images/: Directory containing RGB-D images for each state
 - transitions/: Directory containing transition graph visualizations
 - transitions/transitions.png: Main transition graph visualization
-- transitions/single_block.png: Single block task transition graph
-- transitions/two_blocks.png: Two blocks task transition graph
 - transitions/cup_emptiness.png: Cup emptiness belief task transition graph
 
 Configuration:
@@ -21,8 +20,6 @@
     seed (int): Random seed for reproducibility
     sesame_task_planning_heuristic (str): Heuristic for task planning
     sesame_max_skeletons_optimized (int): Maximum number of skeletons to optimize
-    sesame_use_necessary_atoms (bool): Whether to use necessary atoms in planning
-    sesame_check_expected_atoms (bool): Whether to check expected atoms in planning
 
 Example usage:
     ```python
@@ -31,15 +28,15 @@
     
     # Create objects and predicates
     robot = Object("robot", creator.types["robot"])
-    block = Object("block", creator.types["movable_object"])
+    cup = Object("cup", creator.types["container"])
     
-    # Create initial and goal atoms
+    # Create initial and goal atoms for belief space planning
     init_atoms = {
         GroundAtom(creator.predicates["HandEmpty"], [robot]),
-        GroundAtom(creator.predicates["On"], [block, table])
+        GroundAtom(creator.predicates["ContainingWaterUnknown"], [cup])
     }
     goal_atoms = {
-        GroundAtom(creator.predicates["Inside"], [block, container])
+        GroundAtom(creator.predicates["ContainingWaterKnown"], [cup])
     }
     
     # Plan and visualize transitions
@@ -72,14 +69,6 @@
 from predicators.settings import CFG
 from predicators.planning import task_plan_grounding, task_plan
 
-# Keep the mock action info class but comment it out for now
-# class MockActionExtraInfo:
-#     """Mock action info for mock environment."""
-#     def __init__(self, name: str, objects: Sequence[Object], fn: Any, fn_args: Tuple) -> None:
-#         self.action_name = name
-#         self.operator_objects = objects
-#         self.real_world_fn = fn
-#         self.real_world_fn_args = fn_args
 
 class MockEnvCreatorBase(ABC):
     """Base class for mock environment creators.
@@ -90,6 +79,7 @@ class MockEnvCreatorBase(ABC):
     - Define transitions between states
     - Generate task-specific state sequences
     - Plan and visualize transitions
+    - Support belief space planning when enabled in environment
     
     The environment data is stored in a directory specified by CFG.mock_env_data_dir,
     which is set during initialization. This includes:
@@ -104,7 +94,7 @@ class MockEnvCreatorBase(ABC):
         transitions_dir (str): Directory for transition graph visualizations
         env (MockSpotEnv): Mock Spot environment instance
         types (Dict[str, Type]): Available object types
-        predicates (Dict[str, Predicate]): Available predicates
+        predicates (Dict[str, Predicate]): Available predicates (including belief predicates if enabled)
         options (Dict[str, ParameterizedOption]): Available options
         nsrts (Set[NSRT]): Available NSRTs
         console (Console): Rich console for pretty printing
@@ -136,67 +126,35 @@ def __init__(self, path_dir: str) -> None:
         
         # Get environment info
         self.types: Dict[str, Type] = {t.name: t for t in self.env.types}
+        # Get predicates directly from environment - this will include belief predicates if enabled
         self.predicates: Dict[str, Predicate] = {p.name: p for p in self.env.predicates}
         
-        # Add observation predicates
-        self._add_observation_predicates()
-        
         # Create options
         self.options: Dict[str, ParameterizedOption] = {o.name: o for o in get_gt_options(self.env.get_name())}
         
-        # Add observation options
-        self._add_observation_options()
-        
         # Get NSRTs from factory
         self.nsrts: Set[NSRT] = self._create_nsrts()
 
         # Initialize rich console for pretty printing
         self.console = Console()
 
-    def _add_observation_predicates(self) -> None:
-        """Add predicates for cup observation and belief state."""
-        # Get types
-        robot_type = self.types["robot"]
-        container_type = self.types["container"]
-        movable_type = self.types["movable_object"]
-        
-        # Create observation predicates
-        self.predicates.update({
-            # Keep these predicates but comment out for now
-            # "ContainingWaterUnknown": Predicate("ContainingWaterUnknown", [container_type], lambda s, o: True),
-            # "ContainingWaterKnown": Predicate("ContainingWaterKnown", [container_type], lambda s, o: True),
-            # "ContainingWater": Predicate("ContainingWater", [container_type], lambda s, o: True),
-            # "NotContainingWater": Predicate("NotContainingWater", [container_type], lambda s, o: True),
-            # "InHandViewFromTop": Predicate("InHandViewFromTop", [robot_type, movable_type], lambda s, o: True)
-        })
-
-    def _add_observation_options(self) -> None:
-        """Add options for cup observation."""
-        # TODO: Implement observation options after fixing base pick-place functionality
-        pass
-
     def _create_nsrts(self) -> Set[NSRT]:
-        """Create NSRTs including observation operators."""
-        # Get base NSRTs from factory
+        """Create NSRTs from the environment's predicates and options.
+        
+        Returns NSRTs that can work with both physical and belief predicates
+        (when belief space operators are enabled in the environment).
+        """
+        # Get NSRTs from factory
         factory = MockSpotGroundTruthNSRTFactory()
         base_nsrts = factory.get_nsrts(
             self.env.get_name(),
             self.types,
-            self.predicates,
+            self.predicates,  # This now uses predicates from env including belief ones if enabled
             self.options
         )
         
-        # TODO: Add observation NSRTs after fixing base pick-place functionality
-        # observation_nsrts = self._create_observation_nsrts()
-        # return base_nsrts | observation_nsrts
-        
         return base_nsrts
 
-    def _create_observation_nsrts(self) -> Set[NSRT]:
-        """Create NSRTs for cup observation."""
-        # TODO: Implement observation NSRTs after fixing base pick-place functionality
-        return set()
-
     def plan_and_visualize(self, init_atoms: Set[GroundAtom], goal_atoms: Set[GroundAtom], 
                           objects: Set[Object], output_file: str = "transitions",
                           timeout: float = 10.0) -> None: