Added logging for interventions to monitor the rate of interventions through time

Added an s keyboard command to force success in the case the reward classifier fails

Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>

lerobot/configs/env/so100_real.yaml

@@ -12,10 +12,10 @@ env:
   
   wrapper:
     crop_params_dict:
-      observation.images.front: [126, 43, 329, 518]
+      observation.images.front: [102, 43, 358, 523]
-      observation.images.side: [93, 69, 381, 434]
+      observation.images.side: [92, 123, 379, 349]
-      # observation.images.front: [135, 59, 331, 527]
+      # observation.images.front: [109, 37, 361, 557]
-      # observation.images.side: [79, 47, 397, 450]
+      # observation.images.side: [94, 161, 372, 315]
     resize_size: [128, 128]
     control_time_s: 20
     reset_follower_pos: true

lerobot/configs/policy/hilserl_classifier.yaml

@@ -4,8 +4,9 @@ defaults:
   - _self_
 
 seed: 13
-dataset_repo_id: aractingi/push_cube_square_reward_cropped_resized
+dataset_repo_id: aractingi/push_cube_square_light_reward_cropped_resized
-dataset_root: data/aractingi/push_cube_square_reward_cropped_resized
+# aractingi/push_cube_square_reward_1_cropped_resized
+dataset_root: data/aractingi/push_cube_square_light_reward_cropped_resized
 local_files_only: true
 train_split_proportion: 0.8
 
@@ -26,7 +27,6 @@ training:
   eval_freq: 1  # How often to run validation (in epochs)
   save_freq: 1  # How often to save checkpoints (in epochs)
   save_checkpoint: true
-  # image_keys: ["observation.images.top", "observation.images.wrist"]
   image_keys: ["observation.images.front", "observation.images.side"]
   label_key: "next.reward"
   profile_inference_time: false
@@ -37,8 +37,8 @@ eval:
   num_samples_to_log: 30  # Number of validation samples to log in the table
 
 policy:
-  name: "hilserl/classifier/push_cube_square_reward_cropped_resized" #"hilserl/classifier/pick_place_lego_cube_120
+  name: "hilserl/classifier"
-  model_name: "helper2424/resnet10" # "facebook/convnext-base-224" #"helper2424/resnet10"
+  model_name: "helper2424/resnet10" # "facebook/convnext-base-224
   model_type: "cnn"
   num_cameras: 2 # Has to be len(training.image_keys)
 
@@ -50,4 +50,4 @@ wandb:
 
 device: "mps"
 resume: false
-output_dir: "outputs/classifier/resnet10_frozen"
+output_dir: "outputs/classifier/old_trainer_resnet10_frozen"

lerobot/scripts/server/actor_server.py

@@ -223,6 +223,7 @@ def act_with_policy(cfg: DictConfig, robot: Robot, reward_classifier: nn.Module)
     sum_reward_episode = 0
     list_transition_to_send_to_learner = []
     list_policy_time = []
+    episode_intervention = False
 
     for interaction_step in range(cfg.training.online_steps):
         if interaction_step >= cfg.training.online_step_before_learning:
@@ -252,6 +253,7 @@ def act_with_policy(cfg: DictConfig, robot: Robot, reward_classifier: nn.Module)
         if info["is_intervention"]:
             # TODO: Check the shape
             action = info["action_intervention"]
+            episode_intervention = True
 
         # Check for NaN values in observations
         for key, tensor in obs.items():
@@ -295,11 +297,13 @@ def act_with_policy(cfg: DictConfig, robot: Robot, reward_classifier: nn.Module)
                     interaction_message={
                         "Episodic reward": sum_reward_episode,
                         "Interaction step": interaction_step,
+                        "Episode intervention": int(episode_intervention),
                         **stats,
                     }
                 )
             )
             sum_reward_episode = 0.0
+            episode_intervention = False
             obs, info = online_env.reset()
 
 

lerobot/scripts/server/crop_dataset_roi.py

@@ -245,10 +245,18 @@ if __name__ == "__main__":
     images = {k: v.cpu().permute(1, 2, 0).numpy() for k, v in images.items()}
     images = {k: (v * 255).astype("uint8") for k, v in images.items()}
 
-    rois = select_square_roi_for_images(images)
+    # rois = select_square_roi_for_images(images)
+    rois = {
+        "observation.images.front": [102, 43, 358, 523],
+        "observation.images.side": [92, 123, 379, 349],
+    }
     # rois = {
-    #   "observation.images.front": [126, 43, 329, 518],
+    #     "observation.images.side": (92, 123, 379, 349),
-    #   "observation.images.side": [93, 69, 381, 434],
+    #     "observation.images.front": (109, 37, 361, 557),
+    # }
+    # rois = {
+    #   "observation.images.front": [109, 37, 361, 557],
+    #   "observation.images.side": [94, 161, 372, 315],
     # }
 
     # Print the selected rectangular ROIs

lerobot/scripts/server/gym_manipulator.py

@@ -312,7 +312,7 @@ class RewardWrapper(gym.Wrapper):
         start_time = time.perf_counter()
         with torch.inference_mode():
             reward = (
-                self.reward_classifier.predict_reward(images, threshold=0.5)
+                self.reward_classifier.predict_reward(images, threshold=0.6)
                 if self.reward_classifier is not None
                 else 0.0
             )
@@ -507,6 +507,7 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
             "pause_policy": False,
             "reset_env": False,
             "human_intervention_step": False,
+            "episode_success": False,
         }
         self.event_lock = Lock()  # Thread-safe access to events
         self._init_keyboard_listener()
@@ -528,7 +529,12 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
                         if key == keyboard.Key.right or key == keyboard.Key.esc:
                             print("Right arrow key pressed. Exiting loop...")
                             self.events["exit_early"] = True
-                        elif key == keyboard.Key.space and not self.events["exit_early"]:
+                            return
+                        if hasattr(key, "char") and key.char == "s":
+                            print("Key 's' pressed. Episode success triggered.")
+                            self.events["episode_success"] = True
+                            return
+                        if key == keyboard.Key.space and not self.events["exit_early"]:
                             if not self.events["pause_policy"]:
                                 print(
                                     "Space key pressed. Human intervention required.\n"
@@ -536,15 +542,18 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
                                 )
                                 self.events["pause_policy"] = True
                                 log_say("Human intervention stage. Get ready to take over.", play_sounds=True)
-                            elif self.events["pause_policy"] and not self.events["human_intervention_step"]:
+                                return
+                            if self.events["pause_policy"] and not self.events["human_intervention_step"]:
                                 self.events["human_intervention_step"] = True
                                 print("Space key pressed. Human intervention starting.")
                                 log_say("Starting human intervention.", play_sounds=True)
-                            else:
+                                return
+                            if self.events["pause_policy"] and self.events["human_intervention_step"]:
                                 self.events["pause_policy"] = False
                                 self.events["human_intervention_step"] = False
                                 print("Space key pressed for a third time.")
                                 log_say("Continuing with policy actions.", play_sounds=True)
+                                return
                     except Exception as e:
                         print(f"Error handling key press: {e}")
 
@@ -566,7 +575,6 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
         with self.event_lock:
             if self.events["exit_early"]:
                 terminated_by_keyboard = True
-            # If we need to wait for human intervention, we note that outside the lock.
             pause_policy = self.events["pause_policy"]
 
         if pause_policy:
@@ -580,6 +588,13 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
 
         # Execute the step in the underlying environment
         obs, reward, terminated, truncated, info = self.env.step((policy_action, is_intervention))
+
+        # Override reward and termination if episode success event triggered
+        with self.event_lock:
+            if self.events["episode_success"]:
+                reward = 1
+                terminated_by_keyboard = True
+
         return obs, reward, terminated or terminated_by_keyboard, truncated, info
 
     def reset(self, **kwargs) -> Tuple[Any, Dict]: