chore: enable simplify in ruff lint (#2085)

* fix return type

* improve apply with vertorize op

* Update src/lerobot/datasets/aggregate.py

Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>

docs/source/hilserl.mdx

@@ -95,7 +95,6 @@ class HILSerlProcessorConfig:
 class ObservationConfig:
     add_joint_velocity_to_observation: bool = False    # Add joint velocities to state
     add_current_to_observation: bool = False    # Add motor currents to state
-    add_ee_pose_to_observation: bool = False    # Add end-effector pose to state
     display_cameras: bool = False    # Display camera feeds during execution
 
 class ImagePreprocessingConfig:
@@ -105,7 +104,6 @@ class ImagePreprocessingConfig:
 class GripperConfig:
     use_gripper: bool = True    # Enable gripper control
     gripper_penalty: float = 0.0    # Penalty for inappropriate gripper usage
-    gripper_penalty_in_reward: bool = False    # Include gripper penalty in reward
 
 class ResetConfig:
     fixed_reset_joint_positions: Any | None = None    # Joint positions for reset
@@ -288,7 +286,6 @@ You can enable multiple observation processing features simultaneously:
       "observation": {
         "add_joint_velocity_to_observation": true,
         "add_current_to_observation": true,
-        "add_ee_pose_to_observation": false,
         "display_cameras": false
       }
     }

docs/source/phone_teleop.mdx

@@ -136,13 +136,12 @@ Additionally you can customize mapping or safety limits by editing the processor
   ),
   ```
 
-- The `EEBoundsAndSafety` step clamps EE motion to a workspace and checks for large ee step jumps to ensure safety. The `end_effector_bounds` are the bounds for the EE pose and can be modified to change the workspace. The `max_ee_step_m` and `max_ee_twist_step_rad` are the step limits for the EE pose and can be modified to change the safety limits.
+- The `EEBoundsAndSafety` step clamps EE motion to a workspace and checks for large ee step jumps to ensure safety. The `end_effector_bounds` are the bounds for the EE pose and can be modified to change the workspace. The `max_ee_step_m` are the step limits for the EE pose and can be modified to change the safety limits.
 
   ```examples/phone_to_so100/teleoperate.py
   EEBoundsAndSafety(
       end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
       max_ee_step_m=0.10,
-      max_ee_twist_step_rad=0.50,
   )
   ```
 

docs/source/processors_robots_teleop.mdx

@@ -38,7 +38,7 @@ phone_to_robot_ee_pose_processor = RobotProcessorPipeline[RobotAction, RobotActi
             kinematics=kinematics_solver, end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5}, motor_names=list(robot.bus.motors.keys()),
         ),
         EEBoundsAndSafety(
-            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, max_ee_step_m=0.20, max_ee_twist_step_rad=0.50,
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, max_ee_step_m=0.20,
         ),
         GripperVelocityToJoint(),
     ],

examples/phone_to_so100/record.py

@@ -84,7 +84,6 @@ phone_to_robot_ee_pose_processor = RobotProcessorPipeline[tuple[RobotAction, Rob
         EEBoundsAndSafety(
             end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
             max_ee_step_m=0.20,
-            max_ee_twist_step_rad=0.50,
         ),
         GripperVelocityToJoint(speed_factor=20.0),
     ],

examples/phone_to_so100/teleoperate.py

@@ -67,7 +67,6 @@ phone_to_robot_joints_processor = RobotProcessorPipeline[tuple[RobotAction, Robo
         EEBoundsAndSafety(
             end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
             max_ee_step_m=0.10,
-            max_ee_twist_step_rad=0.50,
         ),
         GripperVelocityToJoint(
             speed_factor=20.0,

examples/so100_to_so100_EE/record.py

@@ -101,7 +101,6 @@ ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservati
         EEBoundsAndSafety(
             end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
             max_ee_step_m=0.10,
-            max_ee_twist_step_rad=0.50,
         ),
         InverseKinematicsEEToJoints(
             kinematics=follower_kinematics_solver,

examples/so100_to_so100_EE/teleoperate.py

@@ -78,7 +78,6 @@ ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservati
         EEBoundsAndSafety(
             end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
             max_ee_step_m=0.10,
-            max_ee_twist_step_rad=0.50,
         ),
         InverseKinematicsEEToJoints(
             kinematics=follower_kinematics_solver,

pyproject.toml

@@ -201,7 +201,7 @@ exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
 # N: pep8-naming
 # TODO: Uncomment rules when ready to use
 select = [
-    "E", "W", "F", "I", "B", "C4", "T20", "N" # "SIM", "A", "S", "D", "RUF", "UP"
+    "E", "W", "F", "I", "B", "C4", "T20", "N", "UP", "SIM" #, "A", "S", "D", "RUF"
 ]
 ignore = [
     "E501", # Line too long

src/lerobot/async_inference/helpers.py

@@ -31,7 +31,6 @@ from lerobot.utils.constants import OBS_IMAGES, OBS_STATE, OBS_STR
 from lerobot.utils.utils import init_logging
 
 Action = torch.Tensor
-ActionChunk = torch.Tensor
 
 # observation as received from the robot
 RawObservation = dict[str, torch.Tensor]
@@ -46,7 +45,7 @@ Observation = dict[str, torch.Tensor]
 def visualize_action_queue_size(action_queue_size: list[int]) -> None:
     import matplotlib.pyplot as plt
 
-    fig, ax = plt.subplots()
+    _, ax = plt.subplots()
     ax.set_title("Action Queue Size Over Time")
     ax.set_xlabel("Environment steps")
     ax.set_ylabel("Action Queue Size")

src/lerobot/cameras/utils.py

@@ -15,14 +15,10 @@
 # limitations under the License.
 
 import platform
-from pathlib import Path
-from typing import TypeAlias
 
 from .camera import Camera
 from .configs import CameraConfig, Cv2Rotation
 
-IndexOrPath: TypeAlias = int | Path
-
 
 def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[str, Camera]:
     cameras = {}

src/lerobot/configs/default.py

@@ -16,9 +16,6 @@
 
 from dataclasses import dataclass, field
 
-from lerobot import (
-    policies,  # noqa: F401
-)
 from lerobot.datasets.transforms import ImageTransformsConfig
 from lerobot.datasets.video_utils import get_safe_default_codec
 

src/lerobot/configs/types.py

@@ -15,7 +15,6 @@
 # https://stackoverflow.com/questions/24481852/serialising-an-enum-member-to-json
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Protocol
 
 
 class FeatureType(str, Enum):
@@ -38,10 +37,6 @@ class NormalizationMode(str, Enum):
     IDENTITY = "IDENTITY"
 
 
-class DictLike(Protocol):
-    def __getitem__(self, key: Any) -> Any: ...
-
-
 @dataclass
 class PolicyFeature:
     type: FeatureType

src/lerobot/datasets/aggregate.py

@@ -93,14 +93,13 @@ def update_data_df(df, src_meta, dst_meta):
         pd.DataFrame: Updated DataFrame with adjusted indices.
     """
 
-    def _update(row):
-        row["episode_index"] = row["episode_index"] + dst_meta.info["total_episodes"]
-        row["index"] = row["index"] + dst_meta.info["total_frames"]
-        task = src_meta.tasks.iloc[row["task_index"]].name
-        row["task_index"] = dst_meta.tasks.loc[task].task_index.item()
-        return row
+    df["episode_index"] = df["episode_index"] + dst_meta.info["total_episodes"]
+    df["index"] = df["index"] + dst_meta.info["total_frames"]
 
-    return df.apply(_update, axis=1)
+    src_task_names = src_meta.tasks.index.take(df["task_index"].to_numpy())
+    df["task_index"] = dst_meta.tasks.loc[src_task_names, "task_index"].to_numpy()
+
+    return df
 
 
 def update_meta_data(
@@ -126,27 +125,21 @@ def update_meta_data(
         pd.DataFrame: Updated DataFrame with adjusted indices and timestamps.
     """
 
-    def _update(row):
-        row["meta/episodes/chunk_index"] = row["meta/episodes/chunk_index"] + meta_idx["chunk"]
-        row["meta/episodes/file_index"] = row["meta/episodes/file_index"] + meta_idx["file"]
-        row["data/chunk_index"] = row["data/chunk_index"] + data_idx["chunk"]
-        row["data/file_index"] = row["data/file_index"] + data_idx["file"]
-        for key, video_idx in videos_idx.items():
-            row[f"videos/{key}/chunk_index"] = row[f"videos/{key}/chunk_index"] + video_idx["chunk"]
-            row[f"videos/{key}/file_index"] = row[f"videos/{key}/file_index"] + video_idx["file"]
-            row[f"videos/{key}/from_timestamp"] = (
-                row[f"videos/{key}/from_timestamp"] + video_idx["latest_duration"]
-            )
-            row[f"videos/{key}/to_timestamp"] = (
-                row[f"videos/{key}/to_timestamp"] + video_idx["latest_duration"]
-            )
+    df["meta/episodes/chunk_index"] = df["meta/episodes/chunk_index"] + meta_idx["chunk"]
+    df["meta/episodes/file_index"] = df["meta/episodes/file_index"] + meta_idx["file"]
+    df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"]
+    df["data/file_index"] = df["data/file_index"] + data_idx["file"]
+    for key, video_idx in videos_idx.items():
+        df[f"videos/{key}/chunk_index"] = df[f"videos/{key}/chunk_index"] + video_idx["chunk"]
+        df[f"videos/{key}/file_index"] = df[f"videos/{key}/file_index"] + video_idx["file"]
+        df[f"videos/{key}/from_timestamp"] = df[f"videos/{key}/from_timestamp"] + video_idx["latest_duration"]
+        df[f"videos/{key}/to_timestamp"] = df[f"videos/{key}/to_timestamp"] + video_idx["latest_duration"]
 
-        row["dataset_from_index"] = row["dataset_from_index"] + dst_meta.info["total_frames"]
-        row["dataset_to_index"] = row["dataset_to_index"] + dst_meta.info["total_frames"]
-        row["episode_index"] = row["episode_index"] + dst_meta.info["total_episodes"]
-        return row
+    df["dataset_from_index"] = df["dataset_from_index"] + dst_meta.info["total_frames"]
+    df["dataset_to_index"] = df["dataset_to_index"] + dst_meta.info["total_frames"]
+    df["episode_index"] = df["episode_index"] + dst_meta.info["total_episodes"]
 
-    return df.apply(_update, axis=1)
+    return df
 
 
 def aggregate_datasets(

src/lerobot/datasets/lerobot_dataset.py

@@ -848,11 +848,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         return item
 
-    def _add_padding_keys(self, item: dict, padding: dict[str, list[bool]]) -> dict:
-        for key, val in padding.items():
-            item[key] = torch.BoolTensor(val)
-        return item
-
     def __len__(self):
         return self.num_frames
 
@@ -1032,7 +1027,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             # Reset episode buffer and clean up temporary images (if not already deleted during video encoding)
             self.clear_episode_buffer(delete_images=len(self.meta.image_keys) > 0)
 
-    def _batch_save_episode_video(self, start_episode: int, end_episode: int | None = None):
+    def _batch_save_episode_video(self, start_episode: int, end_episode: int | None = None) -> None:
         """
         Batch save videos for multiple episodes.
 
@@ -1158,7 +1153,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         }
         return metadata
 
-    def _save_episode_video(self, video_key: str, episode_index: int):
+    def _save_episode_video(self, video_key: str, episode_index: int) -> dict:
         # Encode episode frames into a temporary video
         ep_path = self._encode_temporary_episode_video(video_key, episode_index)
         ep_size_in_mb = get_video_size_in_mb(ep_path)
@@ -1263,7 +1258,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         if self.image_writer is not None:
             self.image_writer.wait_until_done()
 
-    def _encode_temporary_episode_video(self, video_key: str, episode_index: int) -> dict:
+    def _encode_temporary_episode_video(self, video_key: str, episode_index: int) -> Path:
         """
         Use ffmpeg to convert frames stored as png into mp4 videos.
         Note: `encode_video_frames` is a blocking call. Making it asynchronous shouldn't speedup encoding,
@@ -1396,11 +1391,6 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         """
         return {repo_id: i for i, repo_id in enumerate(self.repo_ids)}
 
-    @property
-    def repo_index_to_id(self):
-        """Return the inverse mapping if repo_id_to_index."""
-        return {v: k for k, v in self.repo_id_to_index}
-
     @property
     def fps(self) -> int:
         """Frames per second used during data collection.
@@ -1431,7 +1421,7 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         """Keys to access image and video stream from cameras."""
         keys = []
         for key, feats in self.features.items():
-            if isinstance(feats, (datasets.Image, VideoFrame)):
+            if isinstance(feats, (datasets.Image | VideoFrame)):
                 keys.append(key)
         return keys
 

src/lerobot/datasets/push_dataset_to_hub/utils.py

@@ -13,67 +13,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import inspect
-from concurrent.futures import ThreadPoolExecutor
-from pathlib import Path
 
 import datasets
-import numpy
-import PIL
 import torch
 
-from lerobot.datasets.video_utils import encode_video_frames
-
-
-def concatenate_episodes(ep_dicts):
-    data_dict = {}
-
-    keys = ep_dicts[0].keys()
-    for key in keys:
-        if torch.is_tensor(ep_dicts[0][key][0]):
-            data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts])
-        else:
-            if key not in data_dict:
-                data_dict[key] = []
-            for ep_dict in ep_dicts:
-                for x in ep_dict[key]:
-                    data_dict[key].append(x)
-
-    total_frames = data_dict["frame_index"].shape[0]
-    data_dict["index"] = torch.arange(0, total_frames, 1)
-    return data_dict
-
-
-def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers: int = 4):
-    out_dir = Path(out_dir)
-    out_dir.mkdir(parents=True, exist_ok=True)
-
-    def save_image(img_array, i, out_dir):
-        img = PIL.Image.fromarray(img_array)
-        img.save(str(out_dir / f"frame_{i:06d}.png"), quality=100)
-
-    num_images = len(imgs_array)
-    with ThreadPoolExecutor(max_workers=max_workers) as executor:
-        [executor.submit(save_image, imgs_array[i], i, out_dir) for i in range(num_images)]
-
-
-def get_default_encoding() -> dict:
-    """Returns the default ffmpeg encoding parameters used by `encode_video_frames`."""
-    signature = inspect.signature(encode_video_frames)
-    return {
-        k: v.default
-        for k, v in signature.parameters.items()
-        if v.default is not inspect.Parameter.empty and k in ["vcodec", "pix_fmt", "g", "crf"]
-    }
-
-
-def check_repo_id(repo_id: str) -> None:
-    if len(repo_id.split("/")) != 2:
-        raise ValueError(
-            f"""`repo_id` is expected to contain a community or user id `/` the name of the dataset
-            (e.g. 'lerobot/pusht'), but contains '{repo_id}'."""
-        )
-
 
 # TODO(aliberts): remove
 def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> dict[str, torch.Tensor]:

src/lerobot/datasets/streaming_dataset.py

@@ -298,9 +298,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
 
         return padding_mask
 
-    def make_frame(
-        self, dataset_iterator: Backtrackable, previous_dataset_iterator: Backtrackable | None = None
-    ) -> Generator:
+    def make_frame(self, dataset_iterator: Backtrackable) -> Generator:
         """Makes a frame starting from a dataset iterator"""
         item = next(dataset_iterator)
         item = item_to_torch(item)

src/lerobot/datasets/transforms.py

@@ -120,7 +120,7 @@ class SharpnessJitter(Transform):
         self.sharpness = self._check_input(sharpness)
 
     def _check_input(self, sharpness):
-        if isinstance(sharpness, (int, float)):
+        if isinstance(sharpness, (int | float)):
             if sharpness < 0:
                 raise ValueError("If sharpness is a single number, it must be non negative.")
             sharpness = [1.0 - sharpness, 1.0 + sharpness]

src/lerobot/datasets/utils.py

@@ -21,7 +21,7 @@ from collections import deque
 from collections.abc import Iterable, Iterator
 from pathlib import Path
 from pprint import pformat
-from typing import Any, Deque, Generic, TypeVar
+from typing import Any, Generic, TypeVar
 
 import datasets
 import numpy as np
@@ -67,18 +67,6 @@ DEFAULT_IMAGE_PATH = "images/{image_key}/episode-{episode_index:06d}/frame-{fram
 LEGACY_EPISODES_PATH = "meta/episodes.jsonl"
 LEGACY_EPISODES_STATS_PATH = "meta/episodes_stats.jsonl"
 LEGACY_TASKS_PATH = "meta/tasks.jsonl"
-LEGACY_DEFAULT_VIDEO_PATH = "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4"
-LEGACY_DEFAULT_PARQUET_PATH = "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"
-
-DATASET_CARD_TEMPLATE = """
----
-# Metadata will go there
----
-This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
-
-## {}
-
-"""
 
 DEFAULT_FEATURES = {
     "timestamp": {"dtype": "float32", "shape": (1,), "names": None},
@@ -219,13 +207,13 @@ def serialize_dict(stats: dict[str, torch.Tensor | np.ndarray | dict]) -> dict:
     """
     serialized_dict = {}
     for key, value in flatten_dict(stats).items():
-        if isinstance(value, (torch.Tensor, np.ndarray)):
+        if isinstance(value, (torch.Tensor | np.ndarray)):
             serialized_dict[key] = value.tolist()
-        elif isinstance(value, list) and isinstance(value[0], (int, float, list)):
+        elif isinstance(value, list) and isinstance(value[0], (int | float | list)):
             serialized_dict[key] = value
         elif isinstance(value, np.generic):
             serialized_dict[key] = value.item()
-        elif isinstance(value, (int, float)):
+        elif isinstance(value, (int | float)):
             serialized_dict[key] = value
         else:
             raise NotImplementedError(f"The value '{value}' of type '{type(value)}' is not supported.")
@@ -383,12 +371,6 @@ def load_episodes(local_dir: Path) -> datasets.Dataset:
     return episodes
 
 
-def backward_compatible_episodes_stats(
-    stats: dict[str, dict[str, np.ndarray]], episodes: list[int]
-) -> dict[int, dict[str, dict[str, np.ndarray]]]:
-    return dict.fromkeys(episodes, stats)
-
-
 def load_image_as_numpy(
     fpath: str | Path, dtype: np.dtype = np.float32, channel_first: bool = True
 ) -> np.ndarray:
@@ -1197,7 +1179,7 @@ def item_to_torch(item: dict) -> dict:
         dict: Dictionary with all tensor-like items converted to torch.Tensor.
     """
     for key, val in item.items():
-        if isinstance(val, (np.ndarray, list)) and key not in ["task"]:
+        if isinstance(val, (np.ndarray | list)) and key not in ["task"]:
             # Convert numpy arrays and lists to torch tensors
             item[key] = torch.tensor(val)
     return item
@@ -1271,8 +1253,8 @@ class Backtrackable(Generic[T]):
             raise ValueError("lookahead must be > 0")
 
         self._source: Iterator[T] = iter(iterable)
-        self._back_buf: Deque[T] = deque(maxlen=history)
-        self._ahead_buf: Deque[T] = deque(maxlen=lookahead) if lookahead > 0 else deque()
+        self._back_buf: deque[T] = deque(maxlen=history)
+        self._ahead_buf: deque[T] = deque(maxlen=lookahead) if lookahead > 0 else deque()
         self._cursor: int = 0
         self._history = history
         self._lookahead = lookahead
@@ -1346,12 +1328,6 @@ class Backtrackable(Generic[T]):
         # When cursor<0, slice so the order remains chronological
         return list(self._back_buf)[: self._cursor or None]
 
-    def lookahead_buffer(self) -> list[T]:
-        """
-        Return a copy of the current lookahead buffer.
-        """
-        return list(self._ahead_buf)
-
     def can_peek_back(self, steps: int = 1) -> bool:
         """
         Check if we can go back `steps` items without raising an IndexError.
@@ -1377,31 +1353,6 @@ class Backtrackable(Generic[T]):
         except StopIteration:
             return False
 
-    def reset_cursor(self) -> None:
-        """
-        Reset cursor to the most recent position (equivalent to calling next()
-        until you're back to the latest item).
-        """
-        self._cursor = 0
-
-    def clear_ahead_buffer(self) -> None:
-        """
-        Clear the ahead buffer, discarding any pre-fetched items.
-        """
-        self._ahead_buf.clear()
-
-    def switch_source_iterable(self, new_source: Iterable[T]) -> None:
-        """
-        Switch the source of the backtrackable to a new iterable, keeping the history.
-
-        This is useful when iterating over a sequence of datasets. The history from the
-        previous source is kept, but the lookahead buffer is cleared. The cursor is reset
-        to the present.
-        """
-        self._source = iter(new_source)
-        self.clear_ahead_buffer()
-        self.reset_cursor()
-
 
 def safe_shard(dataset: datasets.IterableDataset, index: int, num_shards: int) -> datasets.Dataset:
     """

src/lerobot/datasets/video_utils.py

@@ -428,7 +428,7 @@ def concatenate_video_files(
     with tempfile.NamedTemporaryFile(mode="w", suffix=".ffconcat", delete=False) as tmp_concatenate_file:
         tmp_concatenate_file.write("ffconcat version 1.0\n")
         for input_path in input_video_paths:
-            tmp_concatenate_file.write(f"file '{str(input_path)}'\n")
+            tmp_concatenate_file.write(f"file '{str(input_path.resolve())}'\n")
         tmp_concatenate_file.flush()
         tmp_concatenate_path = tmp_concatenate_file.name
 
@@ -437,7 +437,9 @@ def concatenate_video_files(
         tmp_concatenate_path, mode="r", format="concat", options={"safe": "0"}
     )  # safe = 0 allows absolute paths as well as relative paths
 
-    tmp_output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_named_file:
+        tmp_output_video_path = tmp_named_file.name
+
     output_container = av.open(
         tmp_output_video_path, mode="w", options={"movflags": "faststart"}
     )  # faststart is to move the metadata to the beginning of the file to speed up loading
@@ -585,19 +587,6 @@ def get_video_pixel_channels(pix_fmt: str) -> int:
         raise ValueError("Unknown format")
 
 
-def get_image_pixel_channels(image: Image):
-    if image.mode == "L":
-        return 1  # Grayscale
-    elif image.mode == "LA":
-        return 2  # Grayscale + Alpha
-    elif image.mode == "RGB":
-        return 3  # RGB
-    elif image.mode == "RGBA":
-        return 4  # RGBA
-    else:
-        raise ValueError("Unknown format")
-
-
 def get_video_duration_in_s(video_path: Path | str) -> float:
     """
     Get the duration of a video file in seconds using PyAV.

src/lerobot/envs/configs.py

@@ -193,7 +193,6 @@ class ObservationConfig:
 
     add_joint_velocity_to_observation: bool = False
     add_current_to_observation: bool = False
-    add_ee_pose_to_observation: bool = False
     display_cameras: bool = False
 
 
@@ -203,7 +202,6 @@ class GripperConfig:
 
     use_gripper: bool = True
     gripper_penalty: float = 0.0
-    gripper_penalty_in_reward: bool = False
 
 
 @dataclass

src/lerobot/envs/libero.py

@@ -35,7 +35,7 @@ def _parse_camera_names(camera_name: str | Sequence[str]) -> list[str]:
     """Normalize camera_name into a non-empty list of strings."""
     if isinstance(camera_name, str):
         cams = [c.strip() for c in camera_name.split(",") if c.strip()]
-    elif isinstance(camera_name, (list, tuple)):
+    elif isinstance(camera_name, (list | tuple)):
         cams = [str(c).strip() for c in camera_name if str(c).strip()]
     else:
         raise TypeError(f"camera_name must be str or sequence[str], got {type(camera_name).__name__}")

src/lerobot/envs/utils.py

@@ -183,10 +183,10 @@ def _(env: Mapping) -> None:
 
 @close_envs.register
 def _(envs: Sequence) -> None:
-    if isinstance(envs, (str, bytes)):
+    if isinstance(envs, (str | bytes)):
         return
     for v in envs:
-        if isinstance(v, Mapping) or isinstance(v, Sequence) and not isinstance(v, (str, bytes)):
+        if isinstance(v, Mapping) or isinstance(v, Sequence) and not isinstance(v, (str | bytes)):
             close_envs(v)
         elif hasattr(v, "close"):
             _close_single_env(v)

src/lerobot/motors/motors_bus.py

@@ -99,12 +99,6 @@ class Motor:
     norm_mode: MotorNormMode
 
 
-class JointOutOfRangeError(Exception):
-    def __init__(self, message="Joint is out of range"):
-        self.message = message
-        super().__init__(self.message)
-
-
 class PortHandler(Protocol):
     def __init__(self, port_name):
         self.is_open: bool
@@ -348,7 +342,7 @@ class MotorsBus(abc.ABC):
             raise TypeError(motors)
 
     def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> list[str]:
-        if isinstance(values, (int, float)):
+        if isinstance(values, (int | float)):
             return dict.fromkeys(self.ids, values)
         elif isinstance(values, dict):
             return {self.motors[motor].id: val for motor, val in values.items()}
@@ -675,7 +669,7 @@ class MotorsBus(abc.ABC):
         """
         if motors is None:
             motors = list(self.motors)
-        elif isinstance(motors, (str, int)):
+        elif isinstance(motors, (str | int)):
             motors = [motors]
         elif not isinstance(motors, list):
             raise TypeError(motors)
@@ -703,7 +697,7 @@ class MotorsBus(abc.ABC):
         """
         if motors is None:
             motors = list(self.motors)
-        elif isinstance(motors, (str, int)):
+        elif isinstance(motors, (str | int)):
             motors = [motors]
         elif not isinstance(motors, list):
             raise TypeError(motors)
@@ -739,7 +733,7 @@ class MotorsBus(abc.ABC):
         """
         if motors is None:
             motors = list(self.motors)
-        elif isinstance(motors, (str, int)):
+        elif isinstance(motors, (str | int)):
             motors = [motors]
         elif not isinstance(motors, list):
             raise TypeError(motors)

src/lerobot/policies/act/modeling_act.py

@@ -398,10 +398,7 @@ class ACT(nn.Module):
                 "actions must be provided when using the variational objective in training mode."
             )
 
-        if OBS_IMAGES in batch:
-            batch_size = batch[OBS_IMAGES][0].shape[0]
-        else:
-            batch_size = batch[OBS_ENV_STATE].shape[0]
+        batch_size = batch[OBS_IMAGES][0].shape[0] if OBS_IMAGES in batch else batch[OBS_ENV_STATE].shape[0]
 
         # Prepare the latent for input to the transformer encoder.
         if self.config.use_vae and ACTION in batch and self.training:

src/lerobot/policies/sac/configuration_sac.py

@@ -139,8 +139,6 @@ class SACConfig(PreTrainedConfig):
     # Training parameter
     # Number of steps for online training
     online_steps: int = 1000000
-    # Seed for the online environment
-    online_env_seed: int = 10000
     # Capacity of the online replay buffer
     online_buffer_capacity: int = 100000
     # Capacity of the offline replay buffer

src/lerobot/policies/sac/modeling_sac.py

@@ -1061,15 +1061,3 @@ class TanhMultivariateNormalDiag(TransformedDistribution):
             x = transform(x)
 
         return x
-
-
-def _convert_normalization_params_to_tensor(normalization_params: dict) -> dict:
-    converted_params = {}
-    for outer_key, inner_dict in normalization_params.items():
-        converted_params[outer_key] = {}
-        for key, value in inner_dict.items():
-            converted_params[outer_key][key] = torch.tensor(value)
-            if "image" in outer_key:
-                converted_params[outer_key][key] = converted_params[outer_key][key].view(3, 1, 1)
-
-    return converted_params

src/lerobot/policies/vqbet/configuration_vqbet.py

@@ -82,7 +82,6 @@ class VQBeTConfig(PreTrainedConfig):
         gpt_n_head: Number of headers of GPT
         gpt_hidden_dim: Size of hidden dimensions of GPT
         dropout: Dropout rate for GPT
-        mlp_hidden_dim: Size of hidden dimensions of offset header / bin prediction headers parts of VQ-BeT
         offset_loss_weight:  A constant that is multiplied to the offset loss
         primary_code_loss_weight: A constant that is multiplied to the primary code prediction loss
         secondary_code_loss_weight: A constant that is multiplied to the secondary code prediction loss
@@ -125,7 +124,6 @@ class VQBeTConfig(PreTrainedConfig):
     gpt_n_head: int = 8
     gpt_hidden_dim: int = 512
     dropout: float = 0.1
-    mlp_hidden_dim: int = 1024
     offset_loss_weight: float = 10000.0
     primary_code_loss_weight: float = 5.0
     secondary_code_loss_weight: float = 0.5

src/lerobot/policies/vqbet/vqbet_utils.py

@@ -231,16 +231,6 @@ class GPT(nn.Module):
             torch.nn.init.zeros_(module.bias)
             torch.nn.init.ones_(module.weight)
 
-    def crop_block_size(self, gpt_block_size):
-        # model surgery to decrease the block size if necessary
-        # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
-        # but want to use a smaller block size for some smaller, simpler model
-        assert gpt_block_size <= self.config.gpt_block_size
-        self.config.gpt_block_size = gpt_block_size
-        self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:gpt_block_size])
-        for block in self.transformer.h:
-            block.attn.bias = block.attn.bias[:, :, :gpt_block_size, :gpt_block_size]
-
     def configure_parameters(self):
         """
         This long function is unfortunately doing something very simple and is being very defensive:
@@ -270,13 +260,11 @@ class GPT(nn.Module):
         param_dict = dict(self.named_parameters())
         inter_params = decay & no_decay
         union_params = decay | no_decay
-        assert len(inter_params) == 0, "parameters {} made it into both decay/no_decay sets!".format(
-            str(inter_params)
+        assert len(inter_params) == 0, (
+            f"parameters {str(inter_params)} made it into both decay/no_decay sets!"
         )
         assert len(param_dict.keys() - union_params) == 0, (
-            "parameters {} were not separated into either decay/no_decay set!".format(
-                str(param_dict.keys() - union_params),
-            )
+            f"parameters {str(param_dict.keys() - union_params)} were not separated into either decay/no_decay set!"
         )
 
         decay = [param_dict[pn] for pn in sorted(decay)]

src/lerobot/processor/delta_action_processor.py

@@ -83,14 +83,12 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
 
     Attributes:
         position_scale: A factor to scale the delta position inputs.
-        rotation_scale: A factor to scale the delta rotation inputs (currently unused).
         noise_threshold: The magnitude below which delta inputs are considered noise
                          and do not trigger an "enabled" state.
     """
 
     # Scale factors for delta movements
     position_scale: float = 1.0
-    rotation_scale: float = 0.0  # No rotation deltas for gamepad/keyboard
     noise_threshold: float = 1e-3  # 1 mm threshold to filter out noise
 
     def action(self, action: RobotAction) -> RobotAction:

src/lerobot/processor/hil_processor.py

@@ -340,7 +340,7 @@ class GripperPenaltyProcessorStep(ComplementaryDataProcessorStep):
         """
         action = self.transition.get(TransitionKey.ACTION)
 
-        raw_joint_positions = complementary_data.get("raw_joint_positions", None)
+        raw_joint_positions = complementary_data.get("raw_joint_positions")
         if raw_joint_positions is None:
             return complementary_data
 

src/lerobot/processor/normalize_processor.py

@@ -119,13 +119,12 @@ class _NormalizationMixin:
                     )
                 self.features = reconstructed
 
-        if self.norm_map:
-            # if keys are strings (JSON), rebuild enum map
-            if all(isinstance(k, str) for k in self.norm_map.keys()):
-                reconstructed = {}
-                for ft_type_str, norm_mode_str in self.norm_map.items():
-                    reconstructed[FeatureType(ft_type_str)] = NormalizationMode(norm_mode_str)
-                self.norm_map = reconstructed
+        # if keys are strings (JSON), rebuild enum map
+        if self.norm_map and all(isinstance(k, str) for k in self.norm_map):
+            reconstructed = {}
+            for ft_type_str, norm_mode_str in self.norm_map.items():
+                reconstructed[FeatureType(ft_type_str)] = NormalizationMode(norm_mode_str)
+            self.norm_map = reconstructed
 
         # Convert stats to tensors and move to the target device once during initialization.
         self.stats = self.stats or {}

src/lerobot/processor/observation_processor.py

@@ -152,7 +152,7 @@ class VanillaObservationProcessorStep(ObservationProcessorStep):
         """
         # Build a new features mapping keyed by the same FeatureType buckets
         # We assume callers already placed features in the correct FeatureType.
-        new_features: dict[PipelineFeatureType, dict[str, PolicyFeature]] = {ft: {} for ft in features.keys()}
+        new_features: dict[PipelineFeatureType, dict[str, PolicyFeature]] = {ft: {} for ft in features}
 
         exact_pairs = {
             "pixels": OBS_IMAGE,

src/lerobot/rl/actor.py

@@ -97,8 +97,6 @@ from .gym_manipulator import (
     step_env_and_process_transition,
 )
 
-ACTOR_SHUTDOWN_TIMEOUT = 30
-
 # Main entry point
 
 

src/lerobot/rl/buffer.py

@@ -176,7 +176,7 @@ class ReplayBuffer:
                     self.complementary_info[key] = torch.empty(
                         (self.capacity, *value_shape), device=self.storage_device
                     )
-                elif isinstance(value, (int, float)):
+                elif isinstance(value, (int | float)):
                     # Handle scalar values similar to reward
                     self.complementary_info[key] = torch.empty((self.capacity,), device=self.storage_device)
                 else:
@@ -223,7 +223,7 @@ class ReplayBuffer:
                     value = complementary_info[key]
                     if isinstance(value, torch.Tensor):
                         self.complementary_info[key][self.position].copy_(value.squeeze(dim=0))
-                    elif isinstance(value, (int, float)):
+                    elif isinstance(value, (int | float)):
                         self.complementary_info[key][self.position] = value
 
         self.position = (self.position + 1) % self.capacity

src/lerobot/rl/crop_dataset_roi.py

@@ -160,7 +160,7 @@ def get_image_from_lerobot_dataset(dataset: LeRobotDataset):
     return image_dict
 
 
-def convert_lerobot_dataset_to_cropper_lerobot_dataset(
+def convert_lerobot_dataset_to_cropped_lerobot_dataset(
     original_dataset: LeRobotDataset,
     crop_params_dict: dict[str, tuple[int, int, int, int]],
     new_repo_id: str,
@@ -190,7 +190,7 @@ def convert_lerobot_dataset_to_cropper_lerobot_dataset(
     # 1. Create a new (empty) LeRobotDataset for writing.
     new_dataset = LeRobotDataset.create(
         repo_id=new_repo_id,
-        fps=original_dataset.fps,
+        fps=int(original_dataset.fps),
         root=new_dataset_root,
         robot_type=original_dataset.meta.robot_type,
         features=original_dataset.meta.info["features"],
@@ -275,6 +275,12 @@ if __name__ == "__main__":
         default="",
         help="The natural language task to describe the dataset.",
     )
+    parser.add_argument(
+        "--new-repo-id",
+        type=str,
+        default=None,
+        help="The repository id for the new cropped and resized dataset. If not provided, it defaults to `repo_id` + '_cropped_resized'.",
+    )
     args = parser.parse_args()
 
     dataset = LeRobotDataset(repo_id=args.repo_id, root=args.root)
@@ -294,10 +300,16 @@ if __name__ == "__main__":
     for key, roi in rois.items():
         print(f"{key}: {roi}")
 
-    new_repo_id = args.repo_id + "_cropped_resized"
-    new_dataset_root = Path(str(dataset.root) + "_cropped_resized")
+    new_repo_id = args.new_repo_id if args.new_repo_id else args.repo_id + "_cropped_resized"
 
-    cropped_resized_dataset = convert_lerobot_dataset_to_cropper_lerobot_dataset(
+    if args.new_repo_id:
+        new_dataset_name = args.new_repo_id.split("/")[-1]
+        # Parent 1: HF user, Parent 2: HF LeRobot Home
+        new_dataset_root = dataset.root.parent.parent / new_dataset_name
+    else:
+        new_dataset_root = Path(str(dataset.root) + "_cropped_resized")
+
+    cropped_resized_dataset = convert_lerobot_dataset_to_cropped_lerobot_dataset(
         original_dataset=dataset,
         crop_params_dict=rois,
         new_repo_id=new_repo_id,

src/lerobot/rl/learner.py

@@ -102,8 +102,6 @@ from lerobot.utils.utils import (
 
 from .learner_service import MAX_WORKERS, SHUTDOWN_TIMEOUT, LearnerService
 
-LOG_PREFIX = "[LEARNER]"
-
 
 @parser.wrap()
 def train_cli(cfg: TrainRLServerPipelineConfig):

src/lerobot/rl/wandb_utils.py

@@ -137,7 +137,7 @@ class WandBLogger:
                 self._wandb.define_metric(new_custom_key, hidden=True)
 
         for k, v in d.items():
-            if not isinstance(v, (int, float, str)):
+            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.'
                 )

src/lerobot/robots/hope_jr/hope_jr_arm.py

@@ -105,7 +105,7 @@ class HopeJrArm(Robot):
     def is_calibrated(self) -> bool:
         return self.bus.is_calibrated
 
-    def calibrate(self, limb_name: str = None) -> None:
+    def calibrate(self) -> None:
         groups = {
             "all": list(self.bus.motors.keys()),
             "shoulder": ["shoulder_pitch", "shoulder_yaw", "shoulder_roll"],

src/lerobot/robots/so100_follower/robot_kinematic_processor.py

@@ -193,16 +193,12 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
     Attributes:
         end_effector_bounds: A dictionary with "min" and "max" keys for position clipping.
         max_ee_step_m: The maximum allowed change in position (in meters) between steps.
-        max_ee_twist_step_rad: The maximum allowed change in orientation (in radians) between steps.
         _last_pos: Internal state storing the last commanded position.
-        _last_twist: Internal state storing the last commanded orientation.
     """
 
     end_effector_bounds: dict
     max_ee_step_m: float = 0.05
-    max_ee_twist_step_rad: float = 0.20
     _last_pos: np.ndarray | None = field(default=None, init=False, repr=False)
-    _last_twist: np.ndarray | None = field(default=None, init=False, repr=False)
 
     def action(self, action: RobotAction) -> RobotAction:
         x = action["ee.x"]
@@ -233,7 +229,6 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
                 raise ValueError(f"EE jump {n:.3f}m > {self.max_ee_step_m}m")
 
         self._last_pos = pos
-        self._last_twist = twist
 
         action["ee.x"] = float(pos[0])
         action["ee.y"] = float(pos[1])
@@ -246,7 +241,6 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
     def reset(self):
         """Resets the last known position and orientation."""
         self._last_pos = None
-        self._last_twist = None
 
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]

src/lerobot/robots/stretch3/configuration_stretch3.py

@@ -49,5 +49,3 @@ class Stretch3RobotConfig(RobotConfig):
             ),
         }
     )
-
-    mock: bool = False

src/lerobot/robots/stretch3/robot_stretch3.py

@@ -164,10 +164,6 @@ class Stretch3Robot(Robot):
         # TODO(aliberts): return action_sent when motion is limited
         return action
 
-    def print_logs(self) -> None:
-        pass
-        # TODO(aliberts): move robot-specific logs logic here
-
     def teleop_safety_stop(self) -> None:
         if self.teleop is not None:
             self.teleop._safety_stop(robot=self)

src/lerobot/scripts/lerobot_record.py

@@ -267,11 +267,7 @@ def record_loop(
                 for t in teleop
                 if isinstance(
                     t,
-                    (
-                        so100_leader.SO100Leader,
-                        so101_leader.SO101Leader,
-                        koch_leader.KochLeader,
-                    ),
+                    (so100_leader.SO100Leader | so101_leader.SO101Leader | koch_leader.KochLeader),
                 )
             ),
             None,

src/lerobot/teleoperators/gamepad/gamepad_utils.py

@@ -52,10 +52,6 @@ class InputController:
         """Get the current movement deltas (dx, dy, dz) in meters."""
         return 0.0, 0.0, 0.0
 
-    def should_quit(self):
-        """Return True if the user has requested to quit."""
-        return not self.running
-
     def update(self):
         """Update controller state - call this once per frame."""
         pass
@@ -198,14 +194,6 @@ class KeyboardController(InputController):
 
         return delta_x, delta_y, delta_z
 
-    def should_quit(self):
-        """Return True if ESC was pressed."""
-        return self.key_states["quit"]
-
-    def should_save(self):
-        """Return True if Enter was pressed (save episode)."""
-        return self.key_states["success"] or self.key_states["failure"]
-
 
 class GamepadController(InputController):
     """Generate motion deltas from gamepad input."""
@@ -351,8 +339,6 @@ class GamepadControllerHID(InputController):
 
         # Button states
         self.buttons = {}
-        self.quit_requested = False
-        self.save_requested = False
 
     def find_device(self):
         """Look for the gamepad device by vendor and product ID."""
@@ -472,11 +458,3 @@ class GamepadControllerHID(InputController):
         delta_z = -self.right_y * self.z_step_size  # Up/down
 
         return delta_x, delta_y, delta_z
-
-    def should_quit(self):
-        """Return True if quit button was pressed."""
-        return self.quit_requested
-
-    def should_save(self):
-        """Return True if save button was pressed."""
-        return self.save_requested

src/lerobot/teleoperators/homunculus/homunculus_arm.py

@@ -18,7 +18,6 @@ import logging
 import threading
 from collections import deque
 from pprint import pformat
-from typing import Deque
 
 import serial
 
@@ -60,7 +59,7 @@ class HomunculusArm(Teleoperator):
         self.n: int = n
         self.alpha: float = 2 / (n + 1)
         # one deque *per joint* so we can inspect raw history if needed
-        self._buffers: dict[str, Deque[int]] = {
+        self._buffers: dict[str, deque[int]] = {
             joint: deque(maxlen=n)
             for joint in (
                 "shoulder_pitch",

src/lerobot/teleoperators/homunculus/homunculus_glove.py

@@ -18,7 +18,6 @@ import logging
 import threading
 from collections import deque
 from pprint import pformat
-from typing import Deque
 
 import serial
 
@@ -97,7 +96,7 @@ class HomunculusGlove(Teleoperator):
         self.n: int = n
         self.alpha: float = 2 / (n + 1)
         # one deque *per joint* so we can inspect raw history if needed
-        self._buffers: dict[str, Deque[int]] = {joint: deque(maxlen=n) for joint in self.joints}
+        self._buffers: dict[str, deque[int]] = {joint: deque(maxlen=n) for joint in self.joints}
         # running EMA value per joint – lazily initialised on first read
         self._ema: dict[str, float | None] = dict.fromkeys(self._buffers)
 

src/lerobot/teleoperators/keyboard/configuration_keyboard.py

@@ -22,8 +22,9 @@ from ..config import TeleoperatorConfig
 @TeleoperatorConfig.register_subclass("keyboard")
 @dataclass
 class KeyboardTeleopConfig(TeleoperatorConfig):
+    """KeyboardTeleopConfig"""
+
     # TODO(Steven): Consider setting in here the keys that we want to capture/listen
-    mock: bool = False
 
 
 @TeleoperatorConfig.register_subclass("keyboard_ee")

src/lerobot/teleoperators/stretch3_gamepad/configuration_stretch3.py

@@ -22,4 +22,4 @@ from ..config import TeleoperatorConfig
 @TeleoperatorConfig.register_subclass("stretch3")
 @dataclass
 class Stretch3GamePadConfig(TeleoperatorConfig):
-    mock: bool = False
+    """Stretch3GamePadConfig"""

src/lerobot/teleoperators/stretch3_gamepad/stretch3_gamepad.py

@@ -112,10 +112,6 @@ class Stretch3GamePad(Teleoperator):
     def send_feedback(self, feedback: np.ndarray) -> None:
         pass
 
-    def print_logs(self) -> None:
-        pass
-        # TODO(aliberts): move robot-specific logs logic here
-
     def disconnect(self) -> None:
         self.api.stop()
         self.is_connected = False

src/lerobot/utils/constants.py

@@ -33,7 +33,6 @@ TRUNCATED = "next.truncated"
 DONE = "next.done"
 
 ROBOTS = "robots"
-ROBOT_TYPE = "robot_type"
 TELEOPERATORS = "teleoperators"
 
 # files & directories

src/lerobot/utils/errors.py

@@ -30,14 +30,3 @@ class DeviceAlreadyConnectedError(ConnectionError):
     ):
         self.message = message
         super().__init__(self.message)
-
-
-class InvalidActionError(ValueError):
-    """Exception raised when an action is already invalid."""
-
-    def __init__(
-        self,
-        message="The action is invalid. Check the value follows what it is expected from the action space.",
-    ):
-        self.message = message
-        super().__init__(self.message)

src/lerobot/utils/transition.py

@@ -63,7 +63,7 @@ def move_transition_to_device(transition: Transition, device: str = "cpu") -> Tr
         for key, val in transition["complementary_info"].items():
             if isinstance(val, torch.Tensor):
                 transition["complementary_info"][key] = val.to(device, non_blocking=non_blocking)
-            elif isinstance(val, (int, float, bool)):
+            elif isinstance(val, (int | float | bool)):
                 transition["complementary_info"][key] = torch.tensor(val, device=device)
             else:
                 raise ValueError(f"Unsupported type {type(val)} for complementary_info[{key}]")

src/lerobot/utils/utils.py

@@ -330,10 +330,6 @@ class TimerManager:
     def history(self) -> list[float]:
         return deepcopy(self._history)
 
-    @property
-    def fps_history(self) -> list[float]:
-        return [1.0 / t for t in self._history]
-
     @property
     def fps_last(self) -> float:
         return 0.0 if self.last == 0 else 1.0 / self.last

src/lerobot/utils/visualization_utils.py

@@ -32,11 +32,8 @@ def init_rerun(session_name: str = "lerobot_control_loop") -> None:
 
 
 def _is_scalar(x):
-    return (
-        isinstance(x, float)
-        or isinstance(x, numbers.Real)
-        or isinstance(x, (np.integer, np.floating))
-        or (isinstance(x, np.ndarray) and x.ndim == 0)
+    return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
+        isinstance(x, np.ndarray) and x.ndim == 0
     )
 
 

tests/artifacts/policies/save_policy_to_safetensors.py

@@ -66,15 +66,13 @@ def get_policy_stats(ds_repo_id: str, policy_name: str, policy_kwargs: dict):
     for key, param in policy.named_parameters():
         if param.requires_grad:
             grad_stats[f"{key}_mean"] = param.grad.mean()
-            grad_stats[f"{key}_std"] = (
-                param.grad.std() if param.grad.numel() > 1 else torch.tensor(float(0.0))
-            )
+            grad_stats[f"{key}_std"] = param.grad.std() if param.grad.numel() > 1 else torch.tensor(0.0)
 
     optimizer.step()
     param_stats = {}
     for key, param in policy.named_parameters():
         param_stats[f"{key}_mean"] = param.mean()
-        param_stats[f"{key}_std"] = param.std() if param.numel() > 1 else torch.tensor(float(0.0))
+        param_stats[f"{key}_std"] = param.std() if param.numel() > 1 else torch.tensor(0.0)
 
     optimizer.zero_grad()
     policy.reset()

tests/conftest.py

@@ -85,7 +85,7 @@ def policy_feature_factory():
 
 def assert_contract_is_typed(features: dict[PipelineFeatureType, dict[str, PolicyFeature]]) -> None:
     assert isinstance(features, dict)
-    assert all(isinstance(k, PipelineFeatureType) for k in features.keys())
+    assert all(isinstance(k, PipelineFeatureType) for k in features)
     assert all(isinstance(v, dict) for v in features.values())
-    assert all(all(isinstance(nk, str) for nk in v.keys()) for v in features.values())
+    assert all(all(isinstance(nk, str) for nk in v) for v in features.values())
     assert all(all(isinstance(nv, PolicyFeature) for nv in v.values()) for v in features.values())

tests/datasets/test_datasets.py

@@ -949,7 +949,7 @@ def test_statistics_metadata_validation(tmp_path, empty_lerobot_dataset_factory)
     # Check that statistics exist for all features
     assert loaded_dataset.meta.stats is not None, "No statistics found"
 
-    for feature_name in features.keys():
+    for feature_name in features:
         assert feature_name in loaded_dataset.meta.stats, f"No statistics for feature '{feature_name}'"
 
         feature_stats = loaded_dataset.meta.stats[feature_name]

tests/policies/test_sac_config.py

@@ -69,7 +69,6 @@ def test_sac_config_default_initialization():
 
     # Training parameters
     assert config.online_steps == 1000000
-    assert config.online_env_seed == 10000
     assert config.online_buffer_capacity == 100000
     assert config.offline_buffer_capacity == 100000
     assert config.async_prefetch is False

tests/processor/test_pipeline.py

@@ -246,7 +246,7 @@ def test_step_through():
     # Ensure all results are dicts (same format as input)
     for result in results:
         assert isinstance(result, dict)
-        assert all(isinstance(k, TransitionKey) for k in result.keys())
+        assert all(isinstance(k, TransitionKey) for k in result)
 
 
 def test_step_through_with_dict():
@@ -770,7 +770,7 @@ class MockStepWithNonSerializableParam(ProcessorStep):
         # Add type validation for multiplier
         if isinstance(multiplier, str):
             raise ValueError(f"multiplier must be a number, got string '{multiplier}'")
-        if not isinstance(multiplier, (int, float)):
+        if not isinstance(multiplier, (int | float)):
             raise TypeError(f"multiplier must be a number, got {type(multiplier).__name__}")
         self.multiplier = float(multiplier)
         self.env = env  # Non-serializable parameter (like gym.Env)
@@ -1623,9 +1623,7 @@ def test_override_with_callables():
 
             # Define a transform function
             def double_values(x):
-                if isinstance(x, (int, float)):
-                    return x * 2
-                elif isinstance(x, torch.Tensor):
+                if isinstance(x, (int | float | torch.Tensor)):
                     return x * 2
                 return x
 
@@ -1797,10 +1795,9 @@ def test_from_pretrained_nonexistent_path():
         )
 
     # Test with a local directory that exists but has no config files
-    with tempfile.TemporaryDirectory() as tmp_dir:
+    with tempfile.TemporaryDirectory() as tmp_dir, pytest.raises(FileNotFoundError):
         # Since the directory exists but has no config, it will raise FileNotFoundError
-        with pytest.raises(FileNotFoundError):
-            DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="processor.json")
+        DataProcessorPipeline.from_pretrained(tmp_dir, config_filename="processor.json")
 
 
 def test_save_load_with_custom_converter_functions():

tests/processor/test_tokenizer_processor.py

@@ -32,10 +32,7 @@ class MockTokenizer:
         **kwargs,
     ) -> dict[str, torch.Tensor]:
         """Mock tokenization that returns deterministic tokens based on text."""
-        if isinstance(text, str):
-            texts = [text]
-        else:
-            texts = text
+        texts = [text] if isinstance(text, str) else text
 
         batch_size = len(texts)
 

tests/robots/test_reachy2.py

@@ -245,14 +245,14 @@ def test_get_observation(reachy2):
     obs = reachy2.get_observation()
 
     expected_keys = set(reachy2.joints_dict)
-    expected_keys.update(f"{v}" for v in REACHY2_VEL.keys() if reachy2.config.with_mobile_base)
+    expected_keys.update(f"{v}" for v in REACHY2_VEL if reachy2.config.with_mobile_base)
     expected_keys.update(reachy2.cameras.keys())
     assert set(obs.keys()) == expected_keys
 
-    for motor in reachy2.joints_dict.keys():
+    for motor in reachy2.joints_dict:
         assert obs[motor] == reachy2.reachy.joints[REACHY2_JOINTS[motor]].present_position
     if reachy2.config.with_mobile_base:
-        for vel in REACHY2_VEL.keys():
+        for vel in REACHY2_VEL:
             assert obs[vel] == reachy2.reachy.mobile_base.odometry[REACHY2_VEL[vel]]
     if reachy2.config.with_left_teleop_camera:
         assert obs["teleop_left"].shape == (
@@ -282,7 +282,7 @@ def test_send_action(reachy2):
         action.update({k: i * 0.1 for i, k in enumerate(REACHY2_VEL.keys(), start=1)})
 
     previous_present_position = {
-        k: reachy2.reachy.joints[REACHY2_JOINTS[k]].present_position for k in reachy2.joints_dict.keys()
+        k: reachy2.reachy.joints[REACHY2_JOINTS[k]].present_position for k in reachy2.joints_dict
     }
     returned = reachy2.send_action(action)
 
@@ -290,7 +290,7 @@ def test_send_action(reachy2):
         assert returned == action
 
     assert reachy2.reachy._goal_position_set_total == len(reachy2.joints_dict)
-    for motor in reachy2.joints_dict.keys():
+    for motor in reachy2.joints_dict:
         expected_pos = action[motor]
         real_pos = reachy2.reachy.joints[REACHY2_JOINTS[motor]].goal_position
         if reachy2.config.max_relative_target is None:

tests/teleoperators/test_reachy2_teleoperator.py

@@ -121,20 +121,20 @@ def test_get_action(reachy2):
     action = reachy2.get_action()
 
     expected_keys = set(reachy2.joints_dict)
-    expected_keys.update(f"{v}" for v in REACHY2_VEL.keys() if reachy2.config.with_mobile_base)
+    expected_keys.update(f"{v}" for v in REACHY2_VEL if reachy2.config.with_mobile_base)
     assert set(action.keys()) == expected_keys
 
-    for motor in reachy2.joints_dict.keys():
+    for motor in reachy2.joints_dict:
         if reachy2.config.use_present_position:
             assert action[motor] == reachy2.reachy.joints[REACHY2_JOINTS[motor]].present_position
         else:
             assert action[motor] == reachy2.reachy.joints[REACHY2_JOINTS[motor]].goal_position
     if reachy2.config.with_mobile_base:
         if reachy2.config.use_present_position:
-            for vel in REACHY2_VEL.keys():
+            for vel in REACHY2_VEL:
                 assert action[vel] == reachy2.reachy.mobile_base.odometry[REACHY2_VEL[vel]]
         else:
-            for vel in REACHY2_VEL.keys():
+            for vel in REACHY2_VEL:
                 assert action[vel] == reachy2.reachy.mobile_base.last_cmd_vel[REACHY2_VEL[vel]]
 
 

tests/utils/test_replay_buffer.py

@@ -121,7 +121,7 @@ def get_tensors_memory_consumption(obj, visited_addresses):
 
     if isinstance(obj, torch.Tensor):
         return get_tensor_memory_consumption(obj)
-    elif isinstance(obj, (list, tuple)):
+    elif isinstance(obj, (list | tuple)):
         for item in obj:
             total_size += get_tensors_memory_consumption(item, visited_addresses)
     elif isinstance(obj, dict):