trim episodes

src/lerobot/async_inference/robot_client.py

@@ -49,18 +49,23 @@ import torch
 
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
-from lerobot.robots import (
-    RobotConfig,  # noqa: F401
+from lerobot.robots import (  # noqa: F401
+    Robot,
+    RobotConfig,
+    bi_so_follower,
+    koch_follower,
     make_robot_from_config,
+    omx_follower,
+    so_follower,
 )
 from lerobot.transport import (
     services_pb2,  # type: ignore
     services_pb2_grpc,  # type: ignore
 )
 from lerobot.transport.utils import grpc_channel_options, send_bytes_in_chunks
-from lerobot.utils.import_utils import register_third_party_plugins
 
 from .configs import RobotClientConfig
+from .constants import SUPPORTED_ROBOTS
 from .helpers import (
     Action,
     FPSTracker,
@@ -480,9 +485,8 @@ class RobotClient:
 def async_client(cfg: RobotClientConfig):
     logging.info(pformat(asdict(cfg)))
 
-    # TODO: Assert if checking robot support is still needed with the plugin system
-    # if cfg.robot.type not in SUPPORTED_ROBOTS:
-    #     raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
+    if cfg.robot.type not in SUPPORTED_ROBOTS:
+        raise ValueError(f"Robot {cfg.robot.type} not yet supported!")
 
     client = RobotClient(cfg)
 
@@ -508,5 +512,4 @@ def async_client(cfg: RobotClientConfig):
 
 
 if __name__ == "__main__":
-    register_third_party_plugins()
     async_client()  # run the client

src/lerobot/datasets/dataset_tools.py

@@ -47,6 +47,7 @@ from lerobot.datasets.utils import (
     DEFAULT_EPISODES_PATH,
     get_parquet_file_size_in_mb,
     load_episodes,
+    load_info,
     update_chunk_file_indices,
     write_info,
     write_stats,
@@ -1774,3 +1775,296 @@ def convert_image_to_video_dataset(
 
     # Return new dataset
     return LeRobotDataset(repo_id=repo_id, root=output_dir)
+
+
+def trim_episodes_by_frames(
+    dataset: LeRobotDataset,
+    episode_frames_to_keep: dict[int, list[int]],
+    output_dir: str | Path | None = None,
+    repo_id: str | None = None,
+) -> LeRobotDataset:
+    """Trim multiple episodes to keep only specific frames.
+
+    This function creates a new dataset where the specified episodes contain only
+    the frames at the given indices. All other episodes are copied as-is.
+
+    Args:
+        dataset: The source LeRobotDataset.
+        episode_frames_to_keep: Dict mapping episode indices to lists of global frame indices to keep.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_trimmed" to original.
+
+    Returns:
+        A new LeRobotDataset with the trimmed episodes.
+    """
+    if not episode_frames_to_keep:
+        raise ValueError("No episodes to trim")
+
+    for ep_idx in episode_frames_to_keep:
+        if ep_idx >= dataset.meta.total_episodes:
+            raise ValueError(f"Episode {ep_idx} does not exist")
+        if not episode_frames_to_keep[ep_idx]:
+            raise ValueError(f"No frames to keep for episode {ep_idx}")
+
+    if repo_id is None:
+        repo_id = f"{dataset.repo_id}_trimmed"
+    output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id
+
+    total_trimmed = sum(len(frames) for frames in episode_frames_to_keep.values())
+    logging.info(f"Trimming {len(episode_frames_to_keep)} episodes, keeping {total_trimmed} frames total")
+
+    # Create new metadata
+    new_meta = LeRobotDatasetMetadata.create(
+        repo_id=repo_id,
+        fps=dataset.meta.fps,
+        features=dataset.meta.features,
+        robot_type=dataset.meta.robot_type,
+        root=output_dir,
+        use_videos=len(dataset.meta.video_keys) > 0,
+    )
+
+    # Build set of all frames to keep (for episodes being trimmed)
+    # and compute new frame counts per episode
+    all_keep_frames: set[int] = set()
+    trimmed_frame_counts: dict[int, int] = {}
+    for ep_idx, frames in episode_frames_to_keep.items():
+        all_keep_frames.update(frames)
+        trimmed_frame_counts[ep_idx] = len(frames)
+
+    # Copy and filter data
+    _copy_and_reindex_data_with_multi_frame_filter(
+        dataset, new_meta, episode_frames_to_keep, all_keep_frames
+    )
+
+    # Handle videos if present
+    if dataset.meta.video_keys:
+        _copy_and_reindex_videos_with_multi_frame_filter(
+            dataset, new_meta, episode_frames_to_keep
+        )
+
+    # Copy episode metadata
+    _copy_and_reindex_episodes_metadata_for_multi_trim(
+        dataset, new_meta, trimmed_frame_counts
+    )
+
+    logging.info(f"Created trimmed dataset with {new_meta.total_frames} frames at {output_dir}")
+    
+    # Return the metadata instead of trying to load as LeRobotDataset
+    # This avoids Hub validation issues when the repo doesn't exist yet
+    return new_meta
+
+
+# Keep old function for backward compatibility
+def trim_episode_by_frames(
+    dataset: LeRobotDataset,
+    episode_index: int,
+    keep_frame_indices: list[int],
+    output_dir: str | Path | None = None,
+    repo_id: str | None = None,
+) -> LeRobotDataset:
+    """Trim a single episode. Wrapper around trim_episodes_by_frames."""
+    return trim_episodes_by_frames(
+        dataset,
+        episode_frames_to_keep={episode_index: keep_frame_indices},
+        output_dir=output_dir,
+        repo_id=repo_id,
+    )
+
+
+def _copy_and_reindex_data_with_multi_frame_filter(
+    src_dataset: LeRobotDataset,
+    dst_meta: LeRobotDatasetMetadata,
+    episode_frames_to_keep: dict[int, list[int]],
+    all_keep_frames: set[int],
+) -> None:
+    """Copy data files with frame-level filtering for multiple episodes."""
+    if src_dataset.meta.episodes is None:
+        src_dataset.meta.episodes = load_episodes(src_dataset.meta.root)
+
+    # Copy tasks
+    if dst_meta.tasks is None and src_dataset.meta.tasks is not None:
+        # Tasks are stored with task string as index
+        dst_meta.save_episode_tasks(list(src_dataset.meta.tasks.index))
+
+    # Get all parquet files
+    data_dir = src_dataset.root / "data"
+    parquet_files = sorted(data_dir.glob("chunk-*/file-*.parquet"))
+
+    trim_episode_set = set(episode_frames_to_keep.keys())
+    global_index = 0
+
+    for parquet_path in tqdm(parquet_files, desc="Processing data files"):
+        df = pd.read_parquet(parquet_path)
+
+        # Filter: keep all frames from non-trimmed episodes, 
+        # and only specified frames from trimmed episodes
+        mask = (~df["episode_index"].isin(trim_episode_set)) | (df["index"].isin(all_keep_frames))
+        df = df[mask].copy().reset_index(drop=True)
+
+        if len(df) == 0:
+            continue
+
+        # Reindex
+        df["index"] = range(global_index, global_index + len(df))
+
+        # Recalculate frame_index within each episode
+        for ep_idx in df["episode_index"].unique():
+            ep_mask = df["episode_index"] == ep_idx
+            df.loc[ep_mask, "frame_index"] = range(ep_mask.sum())
+
+        # Recalculate timestamps based on frame_index and fps
+        df["timestamp"] = df["frame_index"] / src_dataset.meta.fps
+
+        # Determine output path (keep same structure)
+        rel_path = parquet_path.relative_to(src_dataset.root)
+        dst_path = dst_meta.root / rel_path
+        dst_path.parent.mkdir(parents=True, exist_ok=True)
+
+        _write_parquet(df, dst_path, dst_meta)
+        global_index += len(df)
+
+
+def _copy_and_reindex_videos_with_multi_frame_filter(
+    src_dataset: LeRobotDataset,
+    dst_meta: LeRobotDatasetMetadata,
+    episode_frames_to_keep: dict[int, list[int]],
+) -> None:
+    """Copy video files for trimmed dataset.
+    
+    In v3.0 datasets, multiple episodes are concatenated into single video files.
+    Each episode has from_timestamp/to_timestamp indicating its portion of the video.
+    
+    For trimming, we copy the original video files as-is and update the metadata
+    timestamps in _copy_and_reindex_episodes_metadata_for_multi_trim.
+    """
+    for video_key in src_dataset.meta.video_keys:
+        video_dir = src_dataset.root / "videos" / video_key
+        dst_video_dir = dst_meta.root / "videos" / video_key
+        
+        if not video_dir.exists():
+            logging.warning(f"Video directory not found: {video_dir}")
+            continue
+        
+        # Copy all video files (they contain concatenated episodes)
+        # The metadata timestamps will handle which portions to use
+        copied_files = set()
+        for chunk_dir in video_dir.glob("chunk-*"):
+            dst_chunk_dir = dst_video_dir / chunk_dir.name
+            dst_chunk_dir.mkdir(parents=True, exist_ok=True)
+            
+            for video_file in chunk_dir.glob("*.mp4"):
+                if video_file.name not in copied_files:
+                    dst_path = dst_chunk_dir / video_file.name
+                    if not dst_path.exists():
+                        shutil.copy(video_file, dst_path)
+                        copied_files.add(video_file.name)
+        
+        logging.info(f"Copied {len(copied_files)} video files for {video_key}")
+
+
+def _trim_video_frames(
+    src_path: Path,
+    dst_path: Path,
+    keep_frame_indices: list[int],
+    fps: float,
+    episode_start_idx: int,
+) -> None:
+    """Trim a video to keep only specific frames using ffmpeg."""
+    import subprocess
+
+    # Convert global indices to local indices within the episode
+    local_indices = sorted([idx - episode_start_idx for idx in keep_frame_indices])
+
+    if not local_indices:
+        logging.warning(f"No frames to keep for video {src_path}")
+        return
+
+    # Calculate start and end times
+    start_frame = local_indices[0]
+    end_frame = local_indices[-1]
+
+    start_time = start_frame / fps
+    duration = (end_frame - start_frame + 1) / fps
+
+    # Use ffmpeg to trim
+    cmd = [
+        "ffmpeg", "-y",
+        "-ss", str(start_time),
+        "-i", str(src_path),
+        "-t", str(duration),
+        "-c", "copy",  # Fast copy without re-encoding
+        str(dst_path)
+    ]
+
+    try:
+        subprocess.run(cmd, check=True, capture_output=True)
+    except subprocess.CalledProcessError as e:
+        logging.error(f"Failed to trim video: {e.stderr.decode()}")
+        # Fallback: copy the whole video
+        shutil.copy(src_path, dst_path)
+
+
+def _copy_and_reindex_episodes_metadata_for_multi_trim(
+    src_dataset: LeRobotDataset,
+    dst_meta: LeRobotDatasetMetadata,
+    trimmed_frame_counts: dict[int, int],
+) -> None:
+    """Copy and update episode metadata for trimmed dataset."""
+    if src_dataset.meta.episodes is None:
+        src_dataset.meta.episodes = load_episodes(src_dataset.meta.root)
+
+    # Calculate new frame counts and indices
+    episodes_data = []
+    global_idx = 0
+
+    for old_ep_idx in range(src_dataset.meta.total_episodes):
+        src_ep = src_dataset.meta.episodes[old_ep_idx]
+
+        if old_ep_idx in trimmed_frame_counts:
+            ep_length = trimmed_frame_counts[old_ep_idx]
+        else:
+            ep_length = src_ep["length"]
+
+        ep_data = {
+            "episode_index": old_ep_idx,
+            "tasks": src_ep["tasks"],
+            "length": ep_length,
+            "data/chunk_index": src_ep["data/chunk_index"],
+            "data/file_index": src_ep["data/file_index"],
+            "dataset_from_index": global_idx,
+            "dataset_to_index": global_idx + ep_length,
+        }
+
+        # Copy video metadata - preserve timestamps for concatenated videos
+        for video_key in src_dataset.meta.video_keys:
+            ep_data[f"videos/{video_key}/chunk_index"] = src_ep[f"videos/{video_key}/chunk_index"]
+            ep_data[f"videos/{video_key}/file_index"] = src_ep[f"videos/{video_key}/file_index"]
+            
+            # Keep original from_timestamp (start position in concatenated video)
+            orig_from_ts = src_ep[f"videos/{video_key}/from_timestamp"]
+            ep_data[f"videos/{video_key}/from_timestamp"] = orig_from_ts
+            
+            # For trimmed episodes, update to_timestamp based on new length
+            # For non-trimmed episodes, keep original to_timestamp
+            if old_ep_idx in trimmed_frame_counts:
+                ep_data[f"videos/{video_key}/to_timestamp"] = orig_from_ts + (ep_length / src_dataset.meta.fps)
+            else:
+                ep_data[f"videos/{video_key}/to_timestamp"] = src_ep[f"videos/{video_key}/to_timestamp"]
+
+        ep_data["meta/episodes/chunk_index"] = 0
+        ep_data["meta/episodes/file_index"] = 0
+
+        episodes_data.append(ep_data)
+        global_idx += ep_length
+
+    # Save episodes metadata
+    df = pd.DataFrame(episodes_data)
+    episodes_path = dst_meta.root / DEFAULT_EPISODES_PATH.format(chunk_index=0, file_index=0)
+    episodes_path.parent.mkdir(parents=True, exist_ok=True)
+    df.to_parquet(episodes_path)
+
+    # Update info.json
+    info = load_info(src_dataset.root)
+    info["total_episodes"] = len(episodes_data)
+    info["total_frames"] = global_idx
+    write_info(info, dst_meta.root)

src/lerobot/datasets/lerobot_dataset.py

@@ -1771,12 +1771,11 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
             )
         for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
             extra_keys = set(ds.features).difference(intersection_features)
-            if extra_keys:
-                logging.warning(
-                    f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
-                    "other datasets."
-                )
-                self.disabled_features.update(extra_keys)
+            logging.warning(
+                f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
+                "other datasets."
+            )
+            self.disabled_features.update(extra_keys)
 
         self.image_transforms = image_transforms
         self.delta_timestamps = delta_timestamps

src/lerobot/policies/pi0/modeling_pi0.py

@@ -995,14 +995,7 @@ class PI0Policy(PreTrainedPolicy):
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs
-        if _transformers_available:
-            from transformers.modeling_utils import no_init_weights
-
-            with no_init_weights():
-                model = cls(config, **kwargs)
-            model.model.paligemma_with_expert.paligemma.tie_weights()
-        else:
-            model = cls(config, **kwargs)
+        model = cls(config, **kwargs)
 
         # Now manually load and remap the state dict
         try:

src/lerobot/policies/pi05/modeling_pi05.py

@@ -967,14 +967,7 @@ class PI05Policy(PreTrainedPolicy):
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs
-        if _transformers_available:
-            from transformers.modeling_utils import no_init_weights
-
-            with no_init_weights():
-                model = cls(config, **kwargs)
-            model.model.paligemma_with_expert.paligemma.tie_weights()
-        else:
-            model = cls(config, **kwargs)
+        model = cls(config, **kwargs)
 
         # Now manually load and remap the state dict
         try:

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -895,14 +895,7 @@ class PI0FastPolicy(PreTrainedPolicy):
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs
-        if _transformers_available:
-            from transformers.modeling_utils import no_init_weights
-
-            with no_init_weights():
-                model = cls(config, **kwargs)
-            model.model.paligemma_with_expert.paligemma.tie_weights()
-        else:
-            model = cls(config, **kwargs)
+        model = cls(config, **kwargs)
 
         # Now manually load and remap the state dict
         try:

src/lerobot/policies/smolvla/smolvlm_with_expert.py

@@ -77,6 +77,7 @@ class SmolVLMWithExpertModel(nn.Module):
             print(f"Loading  {model_id} weights ...")
             self.vlm = AutoModelForImageTextToText.from_pretrained(
                 model_id,
+                device_map=device,
                 torch_dtype="bfloat16",
                 low_cpu_mem_usage=True,
             )

src/lerobot/scripts/lerobot_calibrate.py

@@ -56,7 +56,6 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
-    openarm_mini,
     so_leader,
     unitree_g1,
 )

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -104,6 +104,28 @@ Convert image dataset to video format and push to hub:
         --operation.type convert_image_to_video \
         --push_to_hub true
 
+Trim single episode to keep only frames within timestamp range:
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht \
+        --new_repo_id lerobot/pusht_trimmed \
+        --operation.type trim_episode \
+        --operation.episode_index 0 \
+        --operation.start_timestamp 10.0 \
+        --operation.end_timestamp 30.0
+
+Trim multiple episodes at once (use null for no limit):
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht \
+        --operation.type trim_episode \
+        --operation.episode_trims '{"0": [10.0, 30.0], "2": [5.0, null], "3": [null, 20.0]}'
+
+Trim and re-upload to same repo (overwrites original):
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht \
+        --operation.type trim_episode \
+        --operation.episode_index 0 \
+        --operation.start_timestamp 10.0 \
+        --push_to_hub true
 Show dataset information:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht_image \
@@ -204,9 +226,32 @@ class InfoConfig(OperationConfig):
     show_features: bool = False
 
 
+@dataclass
+class TrimEpisodeConfig:
+    """Trim episodes to keep only frames within timestamp ranges.
+    
+    Supports multiple episodes via episode_trims dict:
+        --operation.episode_trims '{"0": [10.0, 30.0], "2": [5.0, 20.0]}'
+    
+    Or single episode via legacy parameters:
+        --operation.episode_index 0 --operation.start_timestamp 10.0 --operation.end_timestamp 30.0
+    """
+    type: str = "trim_episode"
+    # Multi-episode support: dict mapping episode_index -> [start_timestamp, end_timestamp]
+    # Use null for no limit, e.g. {"0": [10.0, null], "2": [null, 30.0]}
+    episode_trims: dict[str, list[float | None]] | None = None
+    # Legacy single-episode parameters (used if episode_trims is None)
+    episode_index: int | None = None
+    start_timestamp: float | None = None  # Keep frames from this timestamp (inclusive)
+    end_timestamp: float | None = None    # Keep frames until this timestamp (inclusive)
+
+
 @dataclass
 class EditDatasetConfig:
     repo_id: str
+    operation: (
+        DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig | ConvertImageToVideoConfig | TrimEpisodeConfig
+    )
     operation: OperationConfig
     root: str | None = None
     new_repo_id: str | None = None
@@ -351,6 +396,92 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
         LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()
 
 
+def handle_trim_episode(cfg: EditDatasetConfig) -> None:
+    """Trim episodes to keep only frames within timestamp ranges."""
+    if not isinstance(cfg.operation, TrimEpisodeConfig):
+        raise ValueError("Operation config must be TrimEpisodeConfig")
+
+    # Parse episode trims - support both multi-episode dict and legacy single episode
+    episode_trims: dict[int, tuple[float | None, float | None]] = {}
+    
+    if cfg.operation.episode_trims is not None:
+        # Multi-episode mode
+        for ep_str, ts_range in cfg.operation.episode_trims.items():
+            ep_idx = int(ep_str)
+            start_ts = ts_range[0] if len(ts_range) > 0 else None
+            end_ts = ts_range[1] if len(ts_range) > 1 else None
+            episode_trims[ep_idx] = (start_ts, end_ts)
+    elif cfg.operation.episode_index is not None:
+        # Legacy single-episode mode
+        if cfg.operation.start_timestamp is None and cfg.operation.end_timestamp is None:
+            raise ValueError("At least one of start_timestamp or end_timestamp must be specified")
+        episode_trims[cfg.operation.episode_index] = (
+            cfg.operation.start_timestamp,
+            cfg.operation.end_timestamp,
+        )
+    else:
+        raise ValueError("Either episode_trims or episode_index must be specified")
+
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
+    output_repo_id, output_dir = get_output_path(
+        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
+    )
+
+    if cfg.new_repo_id is None:
+        dataset.root = Path(str(dataset.root) + "_old")
+
+    logging.info(f"Trimming {len(episode_trims)} episode(s) from {cfg.repo_id}")
+
+    # Get episode boundaries and find frames to keep for each episode
+    episodes_info = dataset.meta.episodes
+    all_frames_to_keep: dict[int, list[int]] = {}
+    
+    for ep_idx, (start_ts, end_ts) in episode_trims.items():
+        if ep_idx >= len(episodes_info["episode_index"]):
+            raise ValueError(f"Episode {ep_idx} does not exist (dataset has {len(episodes_info['episode_index'])} episodes)")
+
+        from_frame = episodes_info["dataset_from_index"][ep_idx]
+        to_frame = episodes_info["dataset_to_index"][ep_idx]
+        
+        logging.info(f"Episode {ep_idx}: trimming to [{start_ts}, {end_ts}]")
+        logging.info(f"  Original frames: {from_frame} to {to_frame} ({to_frame - from_frame} frames)")
+
+        # Find frames within timestamp range
+        frames_to_keep = []
+        for frame_idx in range(from_frame, to_frame):
+            frame = dataset.hf_dataset[frame_idx]
+            ts = frame["timestamp"]
+            
+            in_range = True
+            if start_ts is not None and ts < start_ts:
+                in_range = False
+            if end_ts is not None and ts > end_ts:
+                in_range = False
+            
+            if in_range:
+                frames_to_keep.append(frame_idx)
+
+        if not frames_to_keep:
+            raise ValueError(f"Episode {ep_idx}: No frames found in timestamp range [{start_ts}, {end_ts}]")
+
+        logging.info(f"  Keeping {len(frames_to_keep)} frames (indices {frames_to_keep[0]} to {frames_to_keep[-1]})")
+        all_frames_to_keep[ep_idx] = frames_to_keep
+
+    from lerobot.datasets.dataset_tools import trim_episodes_by_frames
+    
+    new_dataset = trim_episodes_by_frames(
+        dataset,
+        episode_frames_to_keep=all_frames_to_keep,
+        output_dir=output_dir,
+        repo_id=output_repo_id,
+    )
+
+    logging.info(f"Dataset saved to {output_dir}")
+    logging.info(f"Episodes: {new_dataset.meta.total_episodes}, Frames: {new_dataset.meta.total_frames}")
+
+    if cfg.push_to_hub:
+        logging.info(f"Pushing to hub as {output_repo_id}")
+        LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()
 def handle_modify_tasks(cfg: EditDatasetConfig) -> None:
     if not isinstance(cfg.operation, ModifyTasksConfig):
         raise ValueError("Operation config must be ModifyTasksConfig")
@@ -515,6 +646,8 @@ def edit_dataset(cfg: EditDatasetConfig) -> None:
         handle_modify_tasks(cfg)
     elif operation_type == "convert_image_to_video":
         handle_convert_image_to_video(cfg)
+    elif operation_type == "trim_episode":
+        handle_trim_episode(cfg)
     elif operation_type == "info":
         handle_info(cfg)
     else:

src/lerobot/scripts/lerobot_find_joint_limits.py

@@ -61,7 +61,6 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
-    openarm_mini,
     so_leader,
 )
 from lerobot.utils.robot_utils import precise_sleep

src/lerobot/scripts/lerobot_record.py

@@ -125,7 +125,6 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
-    openarm_mini,
     reachy2_teleoperator,
     so_leader,
     unitree_g1,
@@ -334,7 +333,6 @@ def record_loop(
         preprocessor.reset()
         postprocessor.reset()
 
-    no_action_count = 0
     timestamp = 0
     start_episode_t = time.perf_counter()
     while timestamp < control_time_s:
@@ -382,13 +380,11 @@ def record_loop(
             act = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
             act_processed_teleop = teleop_action_processor((act, obs))
         else:
-            no_action_count += 1
-            if no_action_count == 1 or no_action_count % 10 == 0:
-                logging.warning(
-                    "No policy or teleoperator provided, skipping action generation. "
-                    "This is likely to happen when resetting the environment without a teleop device. "
-                    "The robot won't be at its rest position at the start of the next episode."
-                )
+            logging.info(
+                "No policy or teleoperator provided, skipping action generation."
+                "This is likely to happen when resetting the environment without a teleop device."
+                "The robot won't be at its rest position at the start of the next episode."
+            )
             continue
 
         # Applies a pipeline to the action, default is IdentityProcessor

src/lerobot/scripts/lerobot_teleoperate.py

@@ -94,7 +94,6 @@ from lerobot.teleoperators import (  # noqa: F401
     make_teleoperator_from_config,
     omx_leader,
     openarm_leader,
-    openarm_mini,
     reachy2_teleoperator,
     so_leader,
     unitree_g1,