feat(envs): add generic observation passthrough

- Add generic observation passthrough in preprocess_observation() for
unhandled ndarray/tensor keys, replacing the pattern of adding per-env
hardcoded key handlers. Extra keys are forwarded as observation.<key>
and can be shaped by env-specific ProcessorSteps via get_env_processors().

src/lerobot/common/wandb_utils.py

@@ -180,32 +180,24 @@ class WandBLogger:
                 self._wandb_custom_step_key.add(new_custom_key)
                 self._wandb.define_metric(new_custom_key, hidden=True)
 
-        batch_data = {}
         for k, v in d.items():
-            # Skip the custom step key here, it's added to the batch below.
-            if custom_step_key is not None and k == custom_step_key:
-                continue
-
-            if isinstance(v, list):
-                for i, elem in enumerate(v):
-                    if isinstance(elem, (int | float)):
-                        batch_data[f"{mode}/{k}_{i}"] = elem
-                continue
-
             if not isinstance(v, (int | float | str)):
                 logging.warning(
                     f'WandB logging of key "{k}" was ignored as its type "{type(v)}" is not handled by this wrapper.'
                 )
                 continue
 
-            batch_data[f"{mode}/{k}"] = v
+            # Do not log the custom step key itself.
+            if self._wandb_custom_step_key is not None and k in self._wandb_custom_step_key:
+                continue
 
-        if batch_data:
             if custom_step_key is not None:
-                batch_data[f"{mode}/{custom_step_key}"] = d[custom_step_key]
-                self._wandb.log(batch_data)
-            else:
-                self._wandb.log(data=batch_data, step=step)
+                value_custom_step = d[custom_step_key]
+                data = {f"{mode}/{k}": v, f"{mode}/{custom_step_key}": value_custom_step}
+                self._wandb.log(data)
+                continue
+
+            self._wandb.log(data={f"{mode}/{k}": v}, step=step)
 
     def log_video(self, video_path: str, step: int, mode: str = "train"):
         if mode not in {"train", "eval"}:

src/lerobot/datasets/io_utils.py

@@ -153,7 +153,7 @@ def cast_stats_to_numpy(stats: dict) -> dict[str, dict[str, np.ndarray]]:
     Returns:
         dict: The statistics dictionary with values cast to numpy arrays.
     """
-    stats = {key: np.atleast_1d(np.array(value)) for key, value in flatten_dict(stats).items()}
+    stats = {key: np.array(value) for key, value in flatten_dict(stats).items()}
     return unflatten_dict(stats)
 
 

src/lerobot/envs/utils.py

@@ -126,6 +126,26 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
     if "camera_obs" in observations:
         return_observations[f"{OBS_STR}.camera_obs"] = observations["camera_obs"]
 
+    # Pass through any remaining ndarray/tensor keys not already handled above,
+    # so env plugins can expose extra observation keys via get_env_processors().
+    _handled = {"pixels", "environment_state", "agent_pos", "robot_state", "policy", "camera_obs"}
+    for key, value in observations.items():
+        if key in _handled:
+            continue
+        target = f"{OBS_STR}.{key}"
+        if target in return_observations:
+            continue
+        if isinstance(value, np.ndarray):
+            val = torch.from_numpy(value).float()
+            if val.dim() == 1:
+                val = val.unsqueeze(0)
+            return_observations[target] = val
+        elif isinstance(value, Tensor):
+            val = value.float()
+            if val.dim() == 1:
+                val = val.unsqueeze(0)
+            return_observations[target] = val
+
     return return_observations
 
 

src/lerobot/utils/import_utils.py

@@ -216,9 +216,15 @@ def register_third_party_plugins() -> None:
 
     This function uses `importlib.metadata` to find packages installed in the environment
     (including editable installs) starting with 'lerobot_robot_', 'lerobot_camera_',
-    'lerobot_teleoperator_', or 'lerobot_policy_' and imports them.
+    'lerobot_teleoperator_', 'lerobot_policy_', or 'lerobot_env_' and imports them.
     """
-    prefixes = ("lerobot_robot_", "lerobot_camera_", "lerobot_teleoperator_", "lerobot_policy_")
+    prefixes = (
+        "lerobot_robot_",
+        "lerobot_camera_",
+        "lerobot_teleoperator_",
+        "lerobot_policy_",
+        "lerobot_env_",
+    )
     imported: list[str] = []
     failed: list[str] = []