make lerobot record easier

src/lerobot/scripts/lerobot_record.py

@@ -487,18 +487,9 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
     robot = make_robot_from_config(cfg.robot)
     teleop = make_teleoperator_from_config(cfg.teleop) if cfg.teleop is not None else None
 
-    # Dataset features derived automatically from robot/teleop pipelines
+    # Dataset features derived automatically from robot/teleop pipelines.
-    if teleop is not None:
+    # When teleop is None (policy-only recording), only observation features are included.
-        dataset_features = build_dataset_features(robot, teleop, use_videos=cfg.dataset.video)
+    dataset_features = build_dataset_features(robot, teleop, use_videos=cfg.dataset.video)
-    else:
-        # Policy-only recording: use robot observation features only
-        from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
-
-        dataset_features = aggregate_pipeline_dataset_features(
-            pipeline=robot.output_pipeline(),
-            initial_features=create_initial_features(observation=robot.raw_observation_features),
-            use_videos=cfg.dataset.video,
-        )
 
     dataset = None
     listener = None

src/lerobot/utils/pipeline_utils.py

@@ -27,7 +27,7 @@ from lerobot.datasets.utils import combine_feature_dicts
 
 def build_dataset_features(
     robot,
-    teleop,
+    teleop=None,
     *,
     use_videos: bool = True,
 ) -> dict:
@@ -35,15 +35,16 @@ def build_dataset_features(
     Derive dataset feature specifications from robot and teleoperator pipelines.
 
     Uses the robot's ``output_pipeline`` and ``raw_observation_features`` to determine
-    what the dataset will store as observations, and the teleoperator's ``output_pipeline``
+    what the dataset will store as observations, and (when provided) the teleoperator's
-    and ``raw_action_features`` to determine what will be stored as actions.
+    ``output_pipeline`` and ``raw_action_features`` to determine what will be stored as actions.
 
     This replaces the old pattern of manually calling ``aggregate_pipeline_dataset_features``
     with explicit processor objects.
 
     Args:
         robot: The robot instance (must have ``output_pipeline()`` and ``raw_observation_features``).
-        teleop: The teleoperator instance (must have ``output_pipeline()`` and ``raw_action_features``).
+        teleop: The teleoperator instance. When ``None`` (policy-only recording), only observation
+            features are returned.
         use_videos: If True, image observations are included as video features.
 
     Returns:
@@ -51,17 +52,19 @@ def build_dataset_features(
 
     Example::
 
-        dataset = LeRobotDataset.create(
+        # Teleop recording
-            repo_id="...",
+        features = build_dataset_features(follower, leader, use_videos=True)
-            fps=30,
+
-            features=build_dataset_features(follower, leader, use_videos=True),
+        # Policy-only recording (no teleop)
-        )
+        features = build_dataset_features(robot, use_videos=True)
     """
     obs_features = aggregate_pipeline_dataset_features(
         pipeline=robot.output_pipeline(),
         initial_features=create_initial_features(observation=robot.raw_observation_features),
         use_videos=use_videos,
     )
+    if teleop is None:
+        return obs_features
     action_features = aggregate_pipeline_dataset_features(
         pipeline=teleop.output_pipeline(),
         initial_features=create_initial_features(action=teleop.raw_action_features),