change the logic to determine whether an object is grasped successfully

tossingbot/envs/pybullet/tasks/toss_objects.py

@@ -70,6 +70,7 @@ def __init__(self,
             "workspace_length": 0.3,
             "workspace_width": 0.4,
             "workspace_position": [0.4, 0],
+            "workspace_thickness": 0.001,
             "box_length": 0.25,
             "box_width": 0.15,
             "box_height": 0.1,
@@ -233,7 +234,7 @@ def load_workspace(self, length=0.3, width=0.4, height=0.02, position=[0.3, 0.0]
             width (float): Width of the workspace.
             position (list): Center position [x, y] of the workspace.
         """
-        thickness = 0.001
+        thickness = self.scene_config['workspace_thickness']
         color = [0.8, 0.8, 0.8, 1.0]
         self.workspace_ids = []
 
@@ -348,7 +349,7 @@ def reset_objects(self, init=False, margin=0.1):
                 p.removeBody(object_id)
 
         self.object_ids = []
-        thickness = 0.001
+        thickness = self.scene_config['workspace_thickness']
 
         for i in range(self.objects_config['n_object']):
             object_type = random.randint(0, len(self.objects_config['object_types']) - 1)
@@ -418,7 +419,7 @@ def pre_simulation_process(self, action):
 
         # Retrieve the depth value from the visual observation
         # grasp_z = self.visual_observation['depth_heightmap'][pixel_y, pixel_x]
-        thickness = 0.001
+        thickness = self.scene_config['workspace_thickness']
         grasp_z = 0.02 + thickness
 
         # Define the grasp pose (position and yaw orientation), post grasp pose, throw pose, and throw velocity
@@ -679,7 +680,8 @@ def check_grasp_success(self):
         if grasp_success:
             object_ids = [
                 object_id for object_id in self.object_ids 
-                if pose_distance(self.get_object_pose(object_id), self.robot.get_tcp_pose())[0] < 0.02
+                if pose_distance(self.get_object_pose(object_id), self.robot.get_tcp_pose())[0] < 0.1 
+                    and self.get_object_pose(object_id)[0][2] > 0.02 + self.scene_config['workspace_thickness'] + 0.001
             ]
             assert len(object_ids) <= 1, "There should be exactly 1 object grasped."
             if len(object_ids) == 0:

tossingbot/experiments/toss_objects/grasp_heurstic.py

@@ -112,7 +112,7 @@ def plot_throw_success(throw_success_history, target_positions):
     use_gui = True
     n_rotations = 1
     phi_deg = 45  # Set phi_deg to 45 degrees
-    total_episodes = 100  # Reduced to a smaller number of episodes for heuristic testing
+    total_episodes = 100
 
     # Create list to track the history of grasp successes, throw successes, and object poses
     grasp_success_history = []
@@ -124,7 +124,8 @@ def plot_throw_success(throw_success_history, target_positions):
     env = TossObjects(
         use_gui=use_gui,
         scene_config={'box_length': 0.15},
-        camera_config={'n_rotations': n_rotations}
+        objects_config={"object_types": ['ball', 'cube']},
+        camera_config={'n_rotations': n_rotations},
     )
 
     # Networks

tossingbot/experiments/toss_objects/test_physics_agent.py

@@ -30,9 +30,9 @@
     n_rotations = 1
     phi_deg = 45
 
-    total_episodes = 10  # Only test for 10 episodes
+    total_episodes = 100  # Test for 100 episodes
 
-    # Lists to track the success rates for the 10 episodes
+    # Lists to track the success rates
     grasp_success_history = []
     throw_success_history = []
 
@@ -96,5 +96,5 @@
     avg_throw_success = np.mean(throw_success_history) if throw_success_history else 0.0
 
     # Print final results
-    print(f"Average Grasp Success Rate: {avg_grasp_success:.3f}")
-    print(f"Average Throw Success Rate (for successful grasps): {avg_throw_success:.3f}")
+    print(f"Average Grasp Success Rate: {avg_grasp_success:.3f}")   # Average Grasp Success Rate: 1.000
+    print(f"Average Throw Success Rate (for successful grasps): {avg_throw_success:.3f}")   # Average Throw Success Rate (for successful grasps): 0.980

tossingbot/experiments/toss_objects/train_physics_agent.py

@@ -117,7 +117,8 @@ def plot_success_rates(avg_grasp_success_history, avg_throw_success_history):
     grasp_criterion = nn.CrossEntropyLoss()
 
     # Optionally load the model
-    start_episode = load_model(agent, optimizer, log_dir)
+    start_episode = 0
+    # start_episode = load_model(agent, optimizer, log_dir, 'phyiscs_agent')
 
     # Main loop
     obs, info = env.reset()