fix(eval): prevent eval_policy crash when start_seed is None and num_envs>1 (#4203)

* fix(eval): align seed list length with num_envs when unseeded

eval_policy appended a single None per batch to all_seeds on the unseeded path while the reward and success lists grew by num_envs. The per-episode zip(..., strict=True) then raised ValueError for num_envs > 1. Extend all_seeds by num_envs so the lists stay aligned.

* chore(tests): delete lerobot_eval test

---------

Co-authored-by: Devin Lai <markauto75@gmail.com>

docs/source/cameras.mdx

@@ -136,6 +136,10 @@ config = RealSenseCameraConfig(
     height=480,
     color_mode=ColorMode.RGB,
     use_depth=True,
+    # Optional fixed color controls. Omit them to leave the current sensor settings unchanged.
+    exposure=120,
+    gain=64,
+    white_balance=4600,
     rotation=Cv2Rotation.NO_ROTATION
 )
 
@@ -154,6 +158,15 @@ finally:
 ```
 <!-- prettier-ignore-end -->
 
+Manual color controls disable the corresponding automatic exposure or white-balance mode. Their
+supported ranges vary by camera model; an invalid value raises an error at connection time that
+includes the range reported by the sensor. Requesting an unsupported control also raises an error.
+Omitted controls leave the sensor's existing automatic or manual setting unchanged. These options
+require `use_rgb=True`.
+
+On the RealSense D405, the color stream is provided by the Stereo Module, so changing manual
+exposure or gain also affects the depth stream.
+
 </hfoption>
 </hfoptions>
 

pyproject.toml

@@ -494,6 +494,19 @@ ignore_errors = true
 module = "lerobot.envs.*"
 ignore_errors = false
 
+[[tool.mypy.overrides]]
+module = "lerobot.annotations.*"
+ignore_errors = false
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
+
+[[tool.mypy.overrides]]
+module = "lerobot.transforms.*"
+ignore_errors = false
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.utils.*"

src/lerobot/cameras/opencv/camera_opencv.py

@@ -120,14 +120,22 @@ class OpenCVCamera(Camera):
         self.rotation: int | None = get_cv2_rotation(config.rotation)
         self.backend: int = config.backend
 
-        if self.height and self.width:
-            self.capture_width, self.capture_height = self.width, self.height
-            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
-                self.capture_width, self.capture_height = self.height, self.width
+        self.capture_width: int | None = None
+        self.capture_height: int | None = None
+        self._reset_connection_settings()
 
     def __str__(self) -> str:
         return f"{self.__class__.__name__}({self.index_or_path})"
 
+    def _reset_connection_settings(self) -> None:
+        """Restore settings that may have been auto-detected during a failed connection."""
+        self.fps = self.config.fps
+        self.width = self.config.width
+        self.height = self.config.height
+        self.capture_width, self.capture_height = self.width, self.height
+        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+            self.capture_width, self.capture_height = self.height, self.width
+
     @property
     def is_connected(self) -> bool:
         """Checks if the camera is currently connected and opened."""
@@ -164,17 +172,25 @@ class OpenCVCamera(Camera):
                 f"Failed to open {self}.Run `lerobot-find-cameras opencv` to find available cameras."
             )
 
-        self._configure_capture_settings()
-        self._start_read_thread()
+        try:
+            self._configure_capture_settings()
+            self._start_read_thread()
 
-        if warmup and self.warmup_s > 0:
-            start_time = time.time()
-            while time.time() - start_time < self.warmup_s:
-                self.async_read(timeout_ms=self.warmup_s * 1000)
-                time.sleep(0.1)
-            with self.frame_lock:
-                if self.latest_frame is None:
-                    raise ConnectionError(f"{self} failed to capture frames during warmup.")
+            if warmup and self.warmup_s > 0:
+                start_time = time.time()
+                while time.time() - start_time < self.warmup_s:
+                    self.async_read(timeout_ms=self.warmup_s * 1000)
+                    time.sleep(0.1)
+                with self.frame_lock:
+                    if self.latest_frame is None:
+                        raise ConnectionError(f"{self} failed to capture frames during warmup.")
+        except BaseException:
+            try:
+                self._cleanup_resources()
+            except Exception:
+                logger.exception(f"Failed to fully clean up {self} after connect() failed.")
+            self._reset_connection_settings()
+            raise
 
         logger.info(f"{self} connected.")
 
@@ -312,32 +328,36 @@ class OpenCVCamera(Camera):
 
         for target in targets_to_scan:
             camera = cv2.VideoCapture(target)
-            if camera.isOpened():
-                default_width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
-                default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                default_fps = camera.get(cv2.CAP_PROP_FPS)
-                default_format = camera.get(cv2.CAP_PROP_FORMAT)
+            try:
+                if camera.isOpened():
+                    default_width = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
+                    default_height = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                    default_fps = camera.get(cv2.CAP_PROP_FPS)
+                    default_format = camera.get(cv2.CAP_PROP_FORMAT)
 
-                # Get FOURCC code and convert to string
-                default_fourcc_code = camera.get(cv2.CAP_PROP_FOURCC)
-                default_fourcc_code_int = int(default_fourcc_code)
-                default_fourcc = "".join([chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)])
+                    # Get FOURCC code and convert to string
+                    default_fourcc_code = camera.get(cv2.CAP_PROP_FOURCC)
+                    default_fourcc_code_int = int(default_fourcc_code)
+                    default_fourcc = "".join(
+                        [chr((default_fourcc_code_int >> 8 * i) & 0xFF) for i in range(4)]
+                    )
 
-                camera_info = {
-                    "name": f"OpenCV Camera @ {target}",
-                    "type": "OpenCV",
-                    "id": target,
-                    "backend_api": camera.getBackendName(),
-                    "default_stream_profile": {
-                        "format": default_format,
-                        "fourcc": default_fourcc,
-                        "width": default_width,
-                        "height": default_height,
-                        "fps": default_fps,
-                    },
-                }
+                    camera_info = {
+                        "name": f"OpenCV Camera @ {target}",
+                        "type": "OpenCV",
+                        "id": target,
+                        "backend_api": camera.getBackendName(),
+                        "default_stream_profile": {
+                            "format": default_format,
+                            "fourcc": default_fourcc,
+                            "width": default_width,
+                            "height": default_height,
+                            "fps": default_fps,
+                        },
+                    }
 
-                found_cameras_info.append(camera_info)
+                    found_cameras_info.append(camera_info)
+            finally:
                 camera.release()
 
         return found_cameras_info
@@ -496,6 +516,26 @@ class OpenCVCamera(Camera):
             self.latest_timestamp = None
             self.new_frame_event.clear()
 
+    def _cleanup_resources(self) -> None:
+        """Stop background reads and release the capture, including after partial setup."""
+        read_thread = self.thread
+        videocapture = self.videocapture
+
+        try:
+            self._stop_read_thread()
+        finally:
+            self.videocapture = None
+            try:
+                if videocapture is not None:
+                    videocapture.release()
+            finally:
+                # Releasing the device may unblock a hardware read that outlived
+                # the first bounded join in _stop_read_thread().
+                if read_thread is not None and read_thread.is_alive():
+                    read_thread.join(timeout=2.0)
+                    if read_thread.is_alive():  # pragma: no cover
+                        logger.warning(f"{self} read thread remained alive after releasing the capture.")
+
     @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
@@ -586,16 +626,6 @@ class OpenCVCamera(Camera):
         if not self.is_connected and self.thread is None:
             raise DeviceNotConnectedError(f"{self} not connected.")
 
-        if self.thread is not None:
-            self._stop_read_thread()
-
-        if self.videocapture is not None:
-            self.videocapture.release()
-            self.videocapture = None
-
-        with self.frame_lock:
-            self.latest_frame = None
-            self.latest_timestamp = None
-            self.new_frame_event.clear()
+        self._cleanup_resources()
 
         logger.info(f"{self} disconnected.")

src/lerobot/cameras/realsense/camera_realsense.py

@@ -121,6 +121,9 @@ class RealSenseCamera(Camera):
 
         self.config = config
 
+        self.width: int | None = config.width
+        self.height: int | None = config.height
+
         if config.serial_number_or_name.isdigit():
             self.serial_number = config.serial_number_or_name
         else:
@@ -131,6 +134,9 @@ class RealSenseCamera(Camera):
         self.use_rgb = config.use_rgb
         self.use_depth = config.use_depth
         self.warmup_s = config.warmup_s
+        self.exposure: int | None = config.exposure
+        self.gain: int | None = config.gain
+        self.white_balance: int | None = config.white_balance
 
         self.rs_pipeline: rs.pipeline | None = None
         self.rs_profile: rs.pipeline_profile | None = None
@@ -145,14 +151,23 @@ class RealSenseCamera(Camera):
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
 
-        if self.height and self.width:
-            self.capture_width, self.capture_height = self.width, self.height
-            if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
-                self.capture_width, self.capture_height = self.height, self.width
+        self.capture_width: int | None = None
+        self.capture_height: int | None = None
+        self._reset_connection_settings()
 
     def __str__(self) -> str:
         return f"{self.__class__.__name__}({self.serial_number})"
 
+    def _reset_connection_settings(self) -> None:
+        """Restore settings that may have been auto-detected during a failed connection."""
+        self.fps = self.config.fps
+        self.width = self.config.width
+        self.height = self.config.height
+        self.warmup_s = self.config.warmup_s
+        self.capture_width, self.capture_height = self.width, self.height
+        if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE]:
+            self.capture_width, self.capture_height = self.height, self.width
+
     @property
     def is_connected(self) -> bool:
         """Checks if the camera pipeline is started and streams are active."""
@@ -172,7 +187,8 @@ class RealSenseCamera(Camera):
 
         Raises:
             DeviceAlreadyConnectedError: If the camera is already connected.
-            ValueError: If the configuration is invalid (e.g., missing serial/name, name not unique).
+            ValueError: If the configuration is invalid, a requested sensor option is unsupported,
+                or a requested sensor value is invalid.
             ConnectionError: If the camera is found but fails to start the pipeline or no RealSense devices are detected at all.
             RuntimeError: If the pipeline starts but fails to apply requested settings.
         """
@@ -190,22 +206,31 @@ class RealSenseCamera(Camera):
                 f"Failed to open {self}.Run `lerobot-find-cameras realsense` to find available cameras."
             ) from e
 
-        self._configure_capture_settings()
-        self._start_read_thread()
+        try:
+            self._configure_capture_settings()
+            self._configure_sensor_options()
+            self._start_read_thread()
 
-        # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
-        self.warmup_s = max(self.warmup_s, 1)
+            # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
+            self.warmup_s = max(self.warmup_s, 1)
 
-        warmup_read = self.async_read if self.use_rgb else self.async_read_depth
-        start_time = time.time()
-        while time.time() - start_time < self.warmup_s:
-            warmup_read(timeout_ms=self.warmup_s * 1000)
-            time.sleep(0.1)
-        with self.frame_lock:
-            if (self.use_rgb and self.latest_color_frame is None) or (
-                self.use_depth and self.latest_depth_frame is None
-            ):
-                raise ConnectionError(f"{self} failed to capture frames during warmup.")
+            warmup_read = self.async_read if self.use_rgb else self.async_read_depth
+            start_time = time.time()
+            while time.time() - start_time < self.warmup_s:
+                warmup_read(timeout_ms=self.warmup_s * 1000)
+                time.sleep(0.1)
+            with self.frame_lock:
+                if (self.use_rgb and self.latest_color_frame is None) or (
+                    self.use_depth and self.latest_depth_frame is None
+                ):
+                    raise ConnectionError(f"{self} failed to capture frames during warmup.")
+        except BaseException:
+            try:
+                self._cleanup_resources()
+            except Exception:
+                logger.exception(f"Failed to fully clean up {self} after connect() failed.")
+            self._reset_connection_settings()
+            raise
 
         logger.info(f"{self} connected.")
 
@@ -339,6 +364,111 @@ class RealSenseCamera(Camera):
         self.new_frame_event.clear()
         return self._async_read(timeout_ms=10000, read_depth=read_depth)
 
+    def _get_color_sensor(self) -> "rs.sensor":
+        """Returns the sensor that controls the color stream.
+
+        Most RealSense cameras expose "RGB Camera" for color. The D405 has no
+        separate RGB module — its color stream comes from "Stereo Module".
+        We try RGB Camera first, then fall back to Stereo Module.
+        """
+        if self.rs_profile is None:
+            raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
+
+        device = self.rs_profile.get_device()
+        sensors = {s.get_info(rs.camera_info.name): s for s in device.query_sensors()}
+
+        for name in ("RGB Camera", "Stereo Module"):
+            if name in sensors:
+                return sensors[name]
+
+        available = list(sensors.keys())
+        raise RuntimeError(f"{self}: no color sensor found. Available sensors: {available}")
+
+    def _set_sensor_option(self, sensor: "rs.sensor", option: "rs.option", value: float, label: str) -> None:
+        """Sets a sensor option, re-raising range errors with actionable diagnostics."""
+        try:
+            sensor.set_option(option, value)
+        except Exception as e:
+            range_info = ""
+            try:
+                option_range = sensor.get_option_range(option)
+                range_info = (
+                    f" (supported range: min={option_range.min}, max={option_range.max}, "
+                    f"step={option_range.step}, default={option_range.default})"
+                )
+            except Exception:
+                range_info = " (option range unavailable)"
+            raise ValueError(
+                f"{self}: failed to set {label} to {value}{range_info}. Original error: {e}"
+            ) from e
+
+    def _configure_sensor_options(self) -> None:
+        """Applies manual sensor options (exposure, gain, white balance) to the color sensor.
+
+        When exposure or gain is set, auto-exposure is disabled first. When white_balance
+        is set, auto white balance is disabled first. An omitted option is left unchanged,
+        and configuration is skipped entirely if all options are omitted.
+
+        Raises:
+            ValueError: If the sensor does not support a requested option or a requested
+                value is invalid. Invalid-value errors include the option name, requested
+                value, and supported range when available.
+        """
+        if self.exposure is None and self.gain is None and self.white_balance is None:
+            return
+
+        color_sensor = self._get_color_sensor()
+
+        requested_options = (
+            (rs.option.exposure, self.exposure, "exposure"),
+            (rs.option.gain, self.gain, "gain"),
+            (rs.option.white_balance, self.white_balance, "white balance"),
+        )
+        unsupported_options = [
+            label
+            for option, value, label in requested_options
+            if value is not None and not color_sensor.supports(option)
+        ]
+        if unsupported_options:
+            raise ValueError(
+                f"{self}: color sensor does not support requested manual options: {unsupported_options}."
+            )
+
+        manual_exposure_requested = self.exposure is not None or self.gain is not None
+        if manual_exposure_requested:
+            if color_sensor.supports(rs.option.enable_auto_exposure):
+                self._set_sensor_option(color_sensor, rs.option.enable_auto_exposure, 0, "auto-exposure")
+                logger.info(f"{self} auto-exposure disabled.")
+            else:
+                logger.warning(
+                    f"{self} sensor does not support disabling auto-exposure; "
+                    "applying manual exposure/gain directly."
+                )
+
+        if self.exposure is not None:
+            self._set_sensor_option(color_sensor, rs.option.exposure, self.exposure, "exposure")
+            logger.info(f"{self} exposure set to {self.exposure}.")
+
+        if self.gain is not None:
+            self._set_sensor_option(color_sensor, rs.option.gain, self.gain, "gain")
+            logger.info(f"{self} gain set to {self.gain}.")
+
+        if self.white_balance is not None:
+            if color_sensor.supports(rs.option.enable_auto_white_balance):
+                self._set_sensor_option(
+                    color_sensor, rs.option.enable_auto_white_balance, 0, "auto white balance"
+                )
+                logger.info(f"{self} auto white balance disabled.")
+            else:
+                logger.warning(
+                    f"{self} sensor does not support disabling auto white balance; "
+                    "applying manual white balance directly."
+                )
+            self._set_sensor_option(
+                color_sensor, rs.option.white_balance, self.white_balance, "white balance"
+            )
+            logger.info(f"{self} white balance set to {self.white_balance}.")
+
     @check_if_not_connected
     def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
         """
@@ -541,6 +671,27 @@ class RealSenseCamera(Camera):
             self.latest_timestamp = None
             self.new_frame_event.clear()
 
+    def _cleanup_resources(self) -> None:
+        """Stop background reads and stop the pipeline, including after partial setup."""
+        read_thread = self.thread
+        rs_pipeline = self.rs_pipeline
+
+        try:
+            self._stop_read_thread()
+        finally:
+            self.rs_pipeline = None
+            self.rs_profile = None
+            try:
+                if rs_pipeline is not None:
+                    rs_pipeline.stop()
+            finally:
+                # Stopping the pipeline may unblock a hardware read that outlived
+                # the first bounded join in _stop_read_thread().
+                if read_thread is not None and read_thread.is_alive():
+                    read_thread.join(timeout=2.0)
+                    if read_thread.is_alive():  # pragma: no cover
+                        logger.warning(f"{self} read thread remained alive after stopping the pipeline.")
+
     def _async_read(self, timeout_ms: float, read_depth: bool = False) -> NDArray[Any]:
         """Shared helper for :meth:`async_read`/:meth:`async_read_depth`: return the latest buffered frame."""
         if self.thread is None or not self.thread.is_alive():
@@ -684,18 +835,5 @@ class RealSenseCamera(Camera):
                 f"Attempted to disconnect {self}, but it appears already disconnected."
             )
 
-        if self.thread is not None:
-            self._stop_read_thread()
-
-        if self.rs_pipeline is not None:
-            self.rs_pipeline.stop()
-            self.rs_pipeline = None
-            self.rs_profile = None
-
-        with self.frame_lock:
-            self.latest_color_frame = None
-            self.latest_depth_frame = None
-            self.latest_timestamp = None
-            self.new_frame_event.clear()
-
+        self._cleanup_resources()
         logger.info(f"{self} disconnected.")

src/lerobot/cameras/realsense/configuration_realsense.py

@@ -46,6 +46,17 @@ class RealSenseCameraConfig(CameraConfig):
         use_depth: Whether to enable depth stream. Defaults to False.
         rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
         warmup_s: Time reading frames before returning from connect (in seconds)
+        exposure: Manual exposure value for the color sensor. When set, auto-exposure is
+            disabled and this fixed value is used. Valid ranges are camera-model specific
+            and reported if the value is rejected. Defaults to None (leave unchanged).
+        gain: Manual gain value for the color sensor. When set, auto-exposure is disabled
+            and this fixed gain is used, which also freezes exposure at its current value
+            when no exposure is configured. Valid ranges are camera-model specific and
+            reported if the value is rejected. Defaults to None (leave unchanged).
+        white_balance: Manual white balance value for the color sensor. When set, auto
+            white balance is disabled and this fixed value is used. Valid ranges are
+            camera-model specific and reported if the value is rejected. Defaults to None
+            (leave unchanged).
 
     Note:
         - Either name or serial_number must be specified.
@@ -61,6 +72,9 @@ class RealSenseCameraConfig(CameraConfig):
     use_depth: bool = False
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
     warmup_s: int = 1
+    exposure: int | None = None
+    gain: int | None = None
+    white_balance: int | None = None
 
     def __post_init__(self) -> None:
         self.color_mode = ColorMode(self.color_mode)
@@ -69,6 +83,18 @@ class RealSenseCameraConfig(CameraConfig):
         if not self.use_rgb and not self.use_depth:
             raise ValueError("At least one of `use_rgb` or `use_depth` must be enabled.")
 
+        manual_color_options = {
+            "exposure": self.exposure,
+            "gain": self.gain,
+            "white_balance": self.white_balance,
+        }
+        configured_color_options = [name for name, value in manual_color_options.items() if value is not None]
+        if configured_color_options and not self.use_rgb:
+            raise ValueError(
+                "Manual color sensor options require `use_rgb=True`. "
+                f"Configured options: {configured_color_options}."
+            )
+
         values = (self.fps, self.width, self.height)
         if any(v is not None for v in values) and any(v is None for v in values):
             raise ValueError(

src/lerobot/configs/dataset.py

@@ -71,13 +71,19 @@ class DatasetRecordConfig:
     # Number of threads per encoder instance. None = auto (codec default).
     # Lower values reduce CPU usage, maps to 'lp' (via svtav1-params) for libsvtav1 and 'threads' for h264/hevc..
     encoder_threads: int | None = None
+    # Skip appending the date-time tag to repo_id, keeping the user-provided name as-is
+    # (e.g. self-managed versioned names intended for a later `lerobot-edit-dataset merge`).
+    no_stamp: bool = False
 
     def stamp_repo_id(self) -> None:
         """Append a date-time tag to ``repo_id`` so each recording session gets a unique name.
 
         Must be called explicitly at dataset *creation* time — not on resume,
         where the existing ``repo_id`` (already stamped) must be preserved.
+        No-op when ``no_stamp`` is set, preserving a user-managed ``repo_id``.
         """
+        if self.no_stamp:
+            return
         if self.repo_id:
             timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
             self.repo_id = f"{self.repo_id}_{timestamp}"

src/lerobot/configs/train.py

@@ -14,6 +14,7 @@
 import builtins
 import datetime as dt
 import json
+import multiprocessing
 import os
 import tempfile
 from dataclasses import dataclass, field
@@ -101,6 +102,12 @@ class TrainPipelineConfig(HubMixin):
     batch_size: int = 8
     prefetch_factor: int = 4
     persistent_workers: bool = True
+    # DataLoader worker start method. "spawn" is safer than "fork" with
+    # non-fork-safe libs (PyAV / torchcodec / ffmpeg), but adds some
+    # worker-startup time per run since workers re-import modules instead
+    # of inheriting parent state. Override with `--dataloader_multiprocessing_context=fork`
+    # when appropriate, or set it to `null` to use Python's platform default.
+    dataloader_multiprocessing_context: str | None = "spawn"
     steps: int = 100_000
     # Run policy in the simulation environment every N steps to measure reward/success (0 = disabled).
     env_eval_freq: int = 20_000
@@ -212,6 +219,17 @@ class TrainPipelineConfig(HubMixin):
             self.reward_model.pretrained_path = str(policy_dir)
 
     def validate(self) -> None:
+        available_contexts = multiprocessing.get_all_start_methods()
+        if (
+            self.dataloader_multiprocessing_context is not None
+            and self.dataloader_multiprocessing_context not in available_contexts
+        ):
+            raise ValueError(
+                "`dataloader_multiprocessing_context` must be None or one of "
+                f"{available_contexts} on this platform, got "
+                f"{self.dataloader_multiprocessing_context!r}."
+            )
+
         self._resolve_pretrained_from_cli()
 
         if self.policy is None and self.reward_model is None:

src/lerobot/datasets/compute_stats.py

@@ -519,13 +519,6 @@ def compute_episode_stats(
         if features[key]["dtype"] in {"string", "language"}:
             continue
 
-        # Features with a zero-width dimension contain no statistics-bearing
-        # values. Skip them like strings instead of letting
-        # get_feature_stats -> RunningQuantileStats.update reshape a size-0 array,
-        # which raises "ValueError: cannot reshape array of size 0".
-        if any(dim == 0 for dim in features[key].get("shape", ())):
-            continue
-
         if features[key]["dtype"] in ["image", "video"]:
             ep_ft_array = sample_images(data)
             axes_to_reduce = (0, 2, 3)

src/lerobot/datasets/dataset_metadata.py

@@ -188,8 +188,8 @@ class LeRobotDatasetMetadata:
     def _load_metadata(self):
         self.info = load_info(self.root)
         check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
-        self.tasks = load_tasks(self.root)
-        self.episodes = load_episodes(self.root)
+        self.tasks = load_tasks(self.root) if self.total_tasks > 0 else None
+        self.episodes = load_episodes(self.root) if self.total_episodes > 0 else None
         self.stats = load_stats(self.root)
 
     def ensure_readable(self) -> None:

src/lerobot/datasets/feature_utils.py

@@ -64,20 +64,12 @@ def get_hf_features_from_features(features: dict) -> datasets.Features:
             continue
         elif ft["dtype"] == "image":
             hf_features[key] = datasets.Image()
-        elif len(ft["shape"]) > 1 and any(dim == 0 for dim in ft["shape"]):
-            raise ValueError(
-                f"Multidimensional features with a zero-width dimension are not supported: "
-                f"'{key}' has shape {ft['shape']}. Only the one-dimensional shape (0,) is supported."
-            )
         elif ft["shape"] == (1,):
             hf_features[key] = datasets.Value(dtype=ft["dtype"])
         elif len(ft["shape"]) == 1:
-            # A zero-width feature (shape=(0,)) has no fixed-size Arrow representation:
-            # pyarrow rejects a fixed-size list of length 0 ("list_size needs to be a
-            # strict positive integer"). Store it as a variable-length sequence
-            # (length=-1) so each per-frame value is simply an empty list.
-            seq_length = ft["shape"][0] if ft["shape"][0] > 0 else -1
-            hf_features[key] = datasets.Sequence(length=seq_length, feature=datasets.Value(dtype=ft["dtype"]))
+            hf_features[key] = datasets.Sequence(
+                length=ft["shape"][0], feature=datasets.Value(dtype=ft["dtype"])
+            )
         elif len(ft["shape"]) == 2:
             hf_features[key] = datasets.Array2D(shape=ft["shape"], dtype=ft["dtype"])
         elif len(ft["shape"]) == 3:

src/lerobot/envs/libero.py

@@ -384,7 +384,12 @@ class LiberoEnv(gym.Env):
 
     def close(self):
         if self._env is not None:
-            self._env.close()
+            try:
+                self._env.close()
+            finally:
+                # LIBERO deletes its inner env on close, so this wrapper must
+                # be recreated before the next reset.
+                self._env = None
 
 
 def _make_env_fns(

src/lerobot/envs/metaworld.py

@@ -155,6 +155,7 @@ class MetaworldEnv(gym.Env):
             env.model.cam_pos[2] = [0.75, 0.075, 0.7]
         env.reset()
         env._freeze_rand_vec = False  # otherwise no randomization
+        env.seeded_rand_vec = True  # use seeded RNG so reset(seed=X) controls object positions
         self._env = env
 
     def render(self) -> np.ndarray:
@@ -220,6 +221,8 @@ class MetaworldEnv(gym.Env):
         self._ensure_env()
         super().reset(seed=seed)
 
+        if seed is not None:
+            self._env.seed(seed)
         raw_obs, info = self._env.reset(seed=seed)
 
         observation = self._format_raw_obs(raw_obs)

src/lerobot/envs/robotwin.py

@@ -384,7 +384,9 @@ class RoboTwinEnv(gym.Env):
 
         self._env: Any | None = None  # deferred — created on first reset() inside worker
         self._step_count: int = 0
-        self._black_frame = np.zeros((self.observation_height, self.observation_width, 3), dtype=np.uint8)
+        self._black_frame: np.ndarray = np.zeros(
+            (self.observation_height, self.observation_width, 3), dtype=np.uint8
+        )
 
         image_spaces = {
             cam: spaces.Box(

src/lerobot/envs/vlabench.py

@@ -373,7 +373,7 @@ class VLABenchEnv(gym.Env):
 
         if action.shape[0] != 7:
             # Unknown layout — fall back to zero-pad so the sim doesn't crash.
-            padded = np.zeros(ctrl_dim, dtype=np.float64)
+            padded: np.ndarray = np.zeros(ctrl_dim, dtype=np.float64)
             padded[: min(action.shape[0], ctrl_dim)] = action[:ctrl_dim]
             return padded
 

src/lerobot/motors/dynamixel/tables.py

@@ -122,6 +122,9 @@ MODEL_ENCODING_TABLE = {
     "xm430-w350": X_SERIES_ENCODINGS_TABLE,
     "xm540-w270": X_SERIES_ENCODINGS_TABLE,
     "xc430-w150": X_SERIES_ENCODINGS_TABLE,
+    "xh540-w150": X_SERIES_ENCODINGS_TABLE,
+    "xc330-t288": X_SERIES_ENCODINGS_TABLE,
+    "xc330-t181": X_SERIES_ENCODINGS_TABLE,
 }
 
 # {model: model_resolution}
@@ -134,6 +137,9 @@ MODEL_RESOLUTION = {
     "xm430-w350": 4096,
     "xm540-w270": 4096,
     "xc430-w150": 4096,
+    "xh540-w150": 4096,
+    "xc330-t288": 4096,
+    "xc330-t181": 4096,
 }
 
 # {model: model_number}
@@ -145,6 +151,9 @@ MODEL_NUMBER_TABLE = {
     "xm430-w350": 1020,
     "xm540-w270": 1120,
     "xc430-w150": 1070,
+    "xh540-w150": 1110,
+    "xc330-t288": 1220,
+    "xc330-t181": 1210,
 }
 
 # {model: available_operating_modes}
@@ -156,6 +165,9 @@ MODEL_OPERATING_MODES = {
     "xm430-w350": [0, 1, 3, 4, 5, 16],
     "xm540-w270": [0, 1, 3, 4, 5, 16],
     "xc430-w150": [1, 3, 4, 16],
+    "xh540-w150": [0, 1, 3, 4, 5, 16],
+    "xc330-t288": [0, 1, 3, 4, 5, 16],
+    "xc330-t181": [0, 1, 3, 4, 5, 16],
 }
 
 MODEL_CONTROL_TABLE = {
@@ -166,6 +178,9 @@ MODEL_CONTROL_TABLE = {
     "xm430-w350": X_SERIES_CONTROL_TABLE,
     "xm540-w270": X_SERIES_CONTROL_TABLE,
     "xc430-w150": X_SERIES_CONTROL_TABLE,
+    "xh540-w150": X_SERIES_CONTROL_TABLE,
+    "xc330-t288": X_SERIES_CONTROL_TABLE,
+    "xc330-t181": X_SERIES_CONTROL_TABLE,
 }
 
 MODEL_BAUDRATE_TABLE = {
@@ -176,6 +191,9 @@ MODEL_BAUDRATE_TABLE = {
     "xm430-w350": X_SERIES_BAUDRATE_TABLE,
     "xm540-w270": X_SERIES_BAUDRATE_TABLE,
     "xc430-w150": X_SERIES_BAUDRATE_TABLE,
+    "xh540-w150": X_SERIES_BAUDRATE_TABLE,
+    "xc330-t288": X_SERIES_BAUDRATE_TABLE,
+    "xc330-t181": X_SERIES_BAUDRATE_TABLE,
 }
 
 AVAILABLE_BAUDRATES = [

src/lerobot/policies/factory.py

@@ -44,12 +44,19 @@ from lerobot.utils.constants import (
     POLICY_PREPROCESSOR_DEFAULT_NAME,
 )
 from lerobot.utils.feature_utils import dataset_to_policy_features
+from lerobot.utils.import_utils import _peft_available, require_package
 
 from .evo1.configuration_evo1 import Evo1Config
 from .groot.configuration_groot import GrootConfig
 from .pretrained import PreTrainedPolicy
 from .utils import validate_visual_features_consistency
 
+if TYPE_CHECKING or _peft_available:
+    from peft import PeftConfig, PeftModel
+else:
+    PeftConfig = None
+    PeftModel = None
+
 
 def _reconnect_relative_absolute_steps(
     preprocessor: PolicyProcessorPipeline, postprocessor: PolicyProcessorPipeline
@@ -177,6 +184,7 @@ def make_pre_post_processors(
             return make_groot_pre_post_processors_from_pretrained(
                 config=policy_cfg,
                 pretrained_path=pretrained_path,
+                revision=pretrained_revision,
                 dataset_stats=kwargs.get("dataset_stats"),
                 dataset_meta=kwargs.get("dataset_meta"),
                 preprocessor_overrides=kwargs.get("preprocessor_overrides"),
@@ -333,12 +341,15 @@ def make_policy(
         # Load a pretrained PEFT model on top of the policy. The pretrained path points to the folder/repo
         # of the adapter and the adapter's config contains the path to the base policy. So we need the
         # adapter config first, then load the correct policy and then apply PEFT.
-        from peft import PeftConfig, PeftModel
+        require_package("peft", extra="peft")
 
         logging.info("Loading policy's PEFT adapter.")
 
         peft_pretrained_path = str(cfg.pretrained_path)
-        peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
+        peft_config = PeftConfig.from_pretrained(
+            peft_pretrained_path,
+            revision=cfg.pretrained_revision,
+        )
 
         kwargs["pretrained_name_or_path"] = peft_config.base_model_name_or_path
         if not kwargs["pretrained_name_or_path"]:
@@ -349,9 +360,14 @@ def make_policy(
                 "the adapter was trained."
             )
 
+        kwargs["revision"] = peft_config.revision
         policy = policy_cls.from_pretrained(**kwargs)
         policy = PeftModel.from_pretrained(
-            policy, peft_pretrained_path, config=peft_config, is_trainable=True
+            policy,
+            peft_pretrained_path,
+            config=peft_config,
+            revision=cfg.pretrained_revision,
+            is_trainable=True,
         )
 
     else:

src/lerobot/policies/gaussian_actor/configuration_gaussian_actor.py

@@ -37,13 +37,19 @@ def is_image_feature(key: str) -> bool:
 @dataclass
 class ConcurrencyConfig:
     """Configuration for the concurrency of the actor and learner.
+
     Possible values are:
     - "threads": Use threads for the actor and learner.
     - "processes": Use processes for the actor and learner.
+
+    ``multiprocessing_context`` selects the process-wide start method when
+    processes are used. Set it to ``None`` to preserve Python's default or a
+    method already selected by the embedding application.
     """
 
     actor: str = "threads"
     learner: str = "threads"
+    multiprocessing_context: str | None = "spawn"
 
 
 @dataclass

src/lerobot/policies/groot/processor_groot.py

@@ -475,6 +475,7 @@ def make_groot_pre_post_processors_from_pretrained(
     config: GrootConfig,
     pretrained_path: str,
     *,
+    revision: str | None = None,
     dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
     dataset_meta: Any | None = None,
     preprocessor_overrides: dict[str, Any] | None = None,
@@ -511,6 +512,7 @@ def make_groot_pre_post_processors_from_pretrained(
 
     preprocessor, postprocessor = _load_groot_processor_pipelines(
         pretrained_path,
+        revision=revision,
         preprocessor_overrides=preprocessor_overrides,
         postprocessor_overrides=postprocessor_overrides,
         preprocessor_config_filename=preprocessor_config_filename,
@@ -526,6 +528,7 @@ def make_groot_pre_post_processors_from_pretrained(
 def _load_groot_processor_pipelines(
     pretrained_path: str,
     *,
+    revision: str | None,
     preprocessor_overrides: dict[str, Any],
     postprocessor_overrides: dict[str, Any],
     preprocessor_config_filename: str,
@@ -540,6 +543,7 @@ def _load_groot_processor_pipelines(
     preprocessor = PolicyProcessorPipeline.from_pretrained(
         pretrained_model_name_or_path=pretrained_path,
         config_filename=preprocessor_config_filename,
+        revision=revision,
         overrides=preprocessor_overrides,
         to_transition=batch_to_transition,
         to_output=transition_to_batch,
@@ -547,6 +551,7 @@ def _load_groot_processor_pipelines(
     postprocessor = PolicyProcessorPipeline.from_pretrained(
         pretrained_model_name_or_path=pretrained_path,
         config_filename=postprocessor_config_filename,
+        revision=revision,
         overrides=postprocessor_overrides,
         to_transition=policy_action_to_transition,
         to_output=transition_to_policy_action,

src/lerobot/policies/molmoact2/modeling_molmoact2.py

@@ -43,11 +43,22 @@ from torch.distributions import Beta
 
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.utils.constants import ACTION
-from lerobot.utils.import_utils import _scipy_available, _transformers_available, require_package
+from lerobot.utils.import_utils import (
+    _peft_available,
+    _scipy_available,
+    _transformers_available,
+    require_package,
+)
 
 from ..rtc.modeling_rtc import RTCProcessor
 from .configuration_molmoact2 import MolmoAct2Config
 
+if TYPE_CHECKING or _peft_available:
+    from peft import LoraConfig, get_peft_model
+else:
+    LoraConfig = None
+    get_peft_model = None
+
 logger = logging.getLogger(__name__)
 
 
@@ -1731,13 +1742,11 @@ class MolmoAct2Policy(PreTrainedPolicy):
 
     def _build_inner_lora_config(self):
         require_package("peft", extra="molmoact2")
-        from peft import LoraConfig
 
         return LoraConfig(**self._get_inner_peft_targets())
 
     def _apply_lora_adapters(self) -> None:
         require_package("peft", extra="molmoact2")
-        from peft import get_peft_model
 
         peft_config = self._build_inner_lora_config()
         self._validate_peft_config(peft_config)

src/lerobot/policies/pretrained.py

@@ -34,14 +34,22 @@ from lerobot.configs import PreTrainedConfig
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.utils.device_utils import resolve_safetensors_device
 from lerobot.utils.hub import HubMixin
+from lerobot.utils.import_utils import _peft_available, require_package
 
 from .utils import log_model_loading_keys
 
-T = TypeVar("T", bound="PreTrainedPolicy")
+if TYPE_CHECKING or _peft_available:
+    from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType, get_peft_model
+else:
+    PEFT_TYPE_TO_CONFIG_MAPPING = None
+    PeftType = None
+    get_peft_model = None
 
 if TYPE_CHECKING:
     from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
 
+T = TypeVar("T", bound="PreTrainedPolicy")
+
 
 def _build_card_context(
     cfg: TrainPipelineConfig | None,
@@ -384,7 +392,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
             peft_cli_overrides: Optional dict of CLI overrides (method_type, target_modules, r, etc.)
                 These are merged with policy defaults to build the final config.
         """
-        from peft import get_peft_model
+        require_package("peft", extra="peft")
 
         # If user provided a complete config, use it directly (with overrides)
         if peft_config is not None:
@@ -455,7 +463,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         Returns:
             Preprocessed dict with renamed keys and init_type mapped to method-specific key.
         """
-        from peft import PeftType
+        require_package("peft", extra="peft")
 
         cli_overrides = cli_overrides.copy()
 
@@ -480,7 +488,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
 
     def _build_peft_config(self, cli_overrides: dict):
         """Build a PEFT config from policy defaults and CLI overrides."""
-        from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType
+        require_package("peft", extra="peft")
 
         # Determine PEFT method type (default to LORA)
         method_type_str = cli_overrides.get("method_type") or "lora"
@@ -507,7 +515,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
 
     def _apply_peft_cli_overrides(self, peft_config, cli_overrides: dict):
         """Apply CLI overrides to an existing PEFT config."""
-        from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType
+        require_package("peft", extra="peft")
 
         # Get method type from existing config or CLI override
         method_type_str = cli_overrides.get("method_type")

src/lerobot/processor/delta_action_processor.py

@@ -132,10 +132,20 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
     ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        for axis in ["x", "y", "z", "gripper"]:
+        for axis in ["x", "y", "z"]:
             features[PipelineFeatureType.ACTION].pop(f"delta_{axis}", None)
+        features[PipelineFeatureType.ACTION].pop("gripper", None)
 
-        for feat in ["enabled", "target_x", "target_y", "target_z", "target_wx", "target_wy", "target_wz"]:
+        for feat in [
+            "enabled",
+            "target_x",
+            "target_y",
+            "target_z",
+            "target_wx",
+            "target_wy",
+            "target_wz",
+            "gripper_vel",
+        ]:
             features[PipelineFeatureType.ACTION][f"{feat}"] = PolicyFeature(
                 type=FeatureType.ACTION, shape=(1,)
             )

src/lerobot/processor/pipeline.py

@@ -713,6 +713,8 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             ProcessorMigrationError: If the model requires migration to processor format.
         """
         model_id = str(pretrained_model_name_or_path)
+        model_path = Path(model_id)
+        is_local_source = model_path.is_dir() or model_path.is_file()
         hub_download_kwargs = {
             "force_download": force_download,
             "resume_download": resume_download,
@@ -731,7 +733,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
 
         # 3. Build steps with overrides
         steps, validated_overrides = cls._build_steps_with_overrides(
-            loaded_config, overrides or {}, model_id, base_path, hub_download_kwargs
+            loaded_config, overrides or {}, model_id, base_path, hub_download_kwargs, is_local_source
         )
 
         # 4. Validate that all overrides were used
@@ -921,6 +923,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         model_id: str,
         base_path: Path | None,
         hub_download_kwargs: dict[str, Any],
+        is_local_source: bool = False,
     ) -> tuple[list[ProcessorStep], set[str]]:
         """Build all processor steps with overrides and state loading.
 
@@ -944,7 +947,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         3. **State Loading** (via _load_step_state):
            - **If step has "state_file"**: Load tensor state from .safetensors
            - **Local first**: Check base_path/state_file.safetensors
-           - **Hub fallback**: Download state file if not found locally
+           - **Hub fallback**: Download state file if the pipeline was loaded from the Hub
            - **Optional**: Only load if step has load_state_dict method
 
         4. **Override Tracking**:
@@ -962,6 +965,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             model_id: The model identifier (needed for Hub state file downloads)
             base_path: Local directory path for finding state files
             hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.)
+            is_local_source: Whether model_id resolved to a local directory or config file.
 
         Returns:
             Tuple of (instantiated_steps_list, unused_override_keys)
@@ -975,7 +979,9 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         steps, remaining_override_keys = cls._build_steps_from_config(loaded_config, overrides)
 
         for step_instance, step_entry in zip(steps, loaded_config["steps"], strict=True):
-            cls._load_step_state(step_instance, step_entry, model_id, base_path, hub_download_kwargs)
+            cls._load_step_state(
+                step_instance, step_entry, model_id, base_path, hub_download_kwargs, is_local_source
+            )
 
         return steps, remaining_override_keys
 
@@ -1139,6 +1145,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         model_id: str,
         base_path: Path | None,
         hub_download_kwargs: dict[str, Any],
+        is_local_source: bool = False,
     ) -> None:
         """Load state dictionary for a processor step if available.
 
@@ -1157,7 +1164,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
            - **Use case**: Loading from local saved model directory
 
         2. **Hub download fallback**: Download state file from repository
-           - **When triggered**: Local file not found or base_path is None
+           - **When triggered**: Local file not found and the pipeline source is a Hub repo
            - **Process**: Use hf_hub_download with same parameters as config
            - **Example**: Download "normalize_step_0.safetensors" from "user/repo"
            - **Result**: Downloaded to local cache, path returned
@@ -1178,6 +1185,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             model_id: The model identifier (used for Hub downloads if needed)
             base_path: Local directory path for finding state files (None for Hub-only)
             hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.)
+            is_local_source: Whether model_id resolved to a local directory or config file.
 
         Note:
             This method modifies step_instance in-place and returns None.
@@ -1191,6 +1199,12 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         # Try local file first
         if base_path and (base_path / state_filename).exists():
             state_path = str(base_path / state_filename)
+        elif is_local_source:
+            state_path = base_path / state_filename if base_path else Path(state_filename)
+            raise FileNotFoundError(
+                f"State file '{state_filename}' was not found for local processor pipeline "
+                f"'{model_id}' at '{state_path}'."
+            )
         else:
             # Download from Hub
             state_path = hf_hub_download(

src/lerobot/rl/actor.py

@@ -91,7 +91,7 @@ from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
 from lerobot.teleoperators.utils import TeleopEvents
 from lerobot.utils.device_utils import get_safe_torch_device
-from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.transition import (
@@ -124,9 +124,7 @@ def actor_cli(cfg: TrainRLServerPipelineConfig):
     cfg.validate()
     display_pid = False
     if not use_threads(cfg):
-        import torch.multiprocessing as mp
-
-        mp.set_start_method("spawn")
+        ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)
         display_pid = True
 
     # Create logs directory to ensure it exists

src/lerobot/rl/learner.py

@@ -102,7 +102,7 @@ from lerobot.utils.constants import (
 )
 from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.io_utils import load_json, write_json
-from lerobot.utils.process import ProcessSignalHandler
+from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
     format_big_number,
@@ -123,9 +123,7 @@ def train_cli(cfg: TrainRLServerPipelineConfig):
     # Fail fast with a friendly error if the optional ``hilserl`` extra is missing.
     require_package("grpcio", extra="hilserl", import_name="grpc")
     if not use_threads(cfg):
-        import torch.multiprocessing as mp
-
-        mp.set_start_method("spawn")
+        ensure_multiprocessing_start_method(cfg.policy.concurrency.multiprocessing_context)
 
     # Use the job_name from the config
     train(

src/lerobot/robots/lekiwi/lekiwi_client.py

@@ -323,6 +323,10 @@ class LeKiwiClient(Robot):
             np.ndarray: the action sent to the motors, potentially clipped.
         """
 
+        # Action values may be torch tensors (e.g. replayed from a dataset) or numpy
+        # scalars; json.dumps only serializes Python primitives, so coerce each value to a
+        # plain float before sending.
+        action = {key: float(value) for key, value in action.items()}
         self.zmq_cmd_socket.send_string(json.dumps(action))  # action is in motor space
 
         # TODO(Steven): Remove the np conversion when it is possible to record a non-numpy array value

src/lerobot/rollout/configs.py

@@ -326,8 +326,17 @@ class RolloutConfig:
 
         policy_path = parser.get_path_arg("policy")
         if policy_path:
-            cli_overrides = parser.get_cli_overrides("policy")
-            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            yaml_overrides = parser.get_yaml_overrides("policy")
+            cli_overrides = parser.get_cli_overrides("policy") or []
+            policy_overrides = yaml_overrides + cli_overrides
+            pretrained_revision = parser.parse_arg("pretrained_revision", cli_overrides)
+            if pretrained_revision is None:
+                pretrained_revision = parser.parse_arg("pretrained_revision", yaml_overrides)
+            self.policy = PreTrainedConfig.from_pretrained(
+                policy_path,
+                revision=pretrained_revision,
+                cli_overrides=policy_overrides,
+            )
             self.policy.pretrained_path = policy_path
         if self.policy is None:
             raise ValueError("--policy.path is required for rollout")

src/lerobot/rollout/context.py

@@ -24,10 +24,11 @@ from __future__ import annotations
 import logging
 from dataclasses import dataclass, field
 from threading import Event
+from typing import TYPE_CHECKING
 
 import torch
 
-from lerobot.configs import FeatureType
+from lerobot.configs import FeatureType, PreTrainedConfig
 from lerobot.datasets import (
     LeRobotDataset,
     aggregate_pipeline_dataset_features,
@@ -47,6 +48,7 @@ from lerobot.processor.relative_action_processor import RelativeActionsProcessor
 from lerobot.robots import make_robot_from_config
 from lerobot.teleoperators import Teleoperator, make_teleoperator_from_config
 from lerobot.utils.feature_utils import combine_feature_dicts, hw_to_dataset_features
+from lerobot.utils.import_utils import _peft_available, require_package
 
 from .configs import BaseStrategyConfig, DAggerStrategyConfig, RolloutConfig
 from .inference import (
@@ -57,6 +59,12 @@ from .inference import (
 )
 from .robot_wrapper import ThreadSafeRobot
 
+if TYPE_CHECKING or _peft_available:
+    from peft import PeftConfig, PeftModel
+else:
+    PeftConfig = None
+    PeftModel = None
+
 logger = logging.getLogger(__name__)
 
 
@@ -159,6 +167,35 @@ class RolloutContext:
 # ---------------------------------------------------------------------------
 
 
+def _load_pretrained_policy(policy_config: PreTrainedConfig) -> PreTrainedPolicy:
+    """Load policy weights, keeping adapter and base-model revisions independent."""
+    pretrained_revision = policy_config.pretrained_revision
+    policy_class = get_policy_class(policy_config.type)
+
+    if not policy_config.use_peft:
+        return policy_class.from_pretrained(
+            policy_config.pretrained_path,
+            config=policy_config,
+            revision=pretrained_revision,
+        )
+
+    require_package("peft", extra="peft")
+
+    peft_path = policy_config.pretrained_path
+    peft_config = PeftConfig.from_pretrained(peft_path, revision=pretrained_revision)
+    policy = policy_class.from_pretrained(
+        pretrained_name_or_path=peft_config.base_model_name_or_path,
+        config=policy_config,
+        revision=peft_config.revision,
+    )
+    return PeftModel.from_pretrained(
+        policy,
+        peft_path,
+        config=peft_config,
+        revision=pretrained_revision,
+    )
+
+
 def build_rollout_context(
     cfg: RolloutConfig,
     shutdown_event: Event,
@@ -176,7 +213,6 @@ def build_rollout_context(
     # --- 1. Policy (heavy I/O, but no hardware yet) -------------------
     logger.info("Loading policy from '%s'...", cfg.policy.pretrained_path)
     policy_config = cfg.policy
-    policy_class = get_policy_class(policy_config.type)
 
     if hasattr(policy_config, "compile_model"):
         policy_config.compile_model = cfg.use_torch_compile
@@ -187,17 +223,7 @@ def build_rollout_context(
             "Please use `cpu` or `cuda` backend."
         )
 
-    if policy_config.use_peft:
-        from peft import PeftConfig, PeftModel
-
-        peft_path = policy_config.pretrained_path
-        peft_config = PeftConfig.from_pretrained(peft_path)
-        policy = policy_class.from_pretrained(
-            pretrained_name_or_path=peft_config.base_model_name_or_path, config=policy_config
-        )
-        policy = PeftModel.from_pretrained(policy, peft_path, config=peft_config)
-    else:
-        policy = policy_class.from_pretrained(policy_config.pretrained_path, config=policy_config)
+    policy = _load_pretrained_policy(policy_config)
 
     if is_rtc:
         policy.config.rtc_config = cfg.inference.rtc
@@ -276,12 +302,22 @@ def build_rollout_context(
     # ``observation_features`` values are either a tuple (camera shape) or the
     # ``float`` type itself used as a sentinel for scalar motor features —
     # see ``dict[str, type | tuple]`` annotation on ``Robot.observation_features``.
+    # Keep cameras (tuple) plus both joint-position (.pos) and base-velocity (.vel)
+    # scalar state features. LeKiwi's observation.state is 9-dim (6 arm .pos +
+    # x/y/theta.vel) and the policy was trained/normalized on all 9; the old .pos-only
+    # filter fed a 6-dim state into a 9-dim normalizer → RuntimeError (size 6 vs 9).
+    # Pure-arm robots have no .vel state keys, so this is a no-op for them.
     observation_features_hw = {
         k: v
         for k, v in all_obs_features.items()
-        if isinstance(v, tuple) or (v is float and k.endswith(".pos"))
+        if isinstance(v, tuple) or (v is float and k.endswith((".pos", ".vel")))
     }
-    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith(".pos")}
+    # Keep both joint-position (.pos) and base-velocity (.vel) action features so
+    # mobile manipulators command the base too (e.g. LeKiwi: 6 arm .pos +
+    # x/y/theta.vel = 9-dim action). Pure-arm robots have no .vel keys, so this is
+    # a no-op for them. Without the .vel keys the base velocities are silently
+    # dropped from dataset_features[ACTION]/ordered_action_keys and the base never moves.
+    action_features_hw = {k: v for k, v in robot.action_features.items() if k.endswith((".pos", ".vel"))}
 
     # The action side is always needed: sync inference reads action names from
     # ``dataset_features[ACTION]`` to map policy tensors back to robot actions.
@@ -392,6 +428,7 @@ def build_rollout_context(
     preprocessor, postprocessor = make_pre_post_processors(
         policy_cfg=policy_config,
         pretrained_path=cfg.policy.pretrained_path,
+        pretrained_revision=policy_config.pretrained_revision,
         dataset_stats=dataset_stats,
         preprocessor_overrides={
             "device_processor": {"device": cfg.device},

src/lerobot/scripts/convert_dataset_v21_to_v30.py

@@ -94,6 +94,8 @@ from lerobot.datasets.video_utils import concatenate_video_files, get_video_dura
 from lerobot.utils.constants import HF_LEROBOT_HOME
 from lerobot.utils.utils import flatten_dict, init_logging
 
+logger = logging.getLogger(__name__)
+
 V21 = "v2.1"
 V30 = "v3.0"
 
@@ -476,11 +478,11 @@ def convert_dataset(
     # First check if the dataset already has a v3.0 version
     if root is None and not force_conversion:
         try:
-            print("Trying to download v3.0 version of the dataset from the hub...")
+            logger.info("Trying to download v3.0 version of the dataset from the hub...")
             snapshot_download(repo_id, repo_type="dataset", revision=V30, local_dir=HF_LEROBOT_HOME / repo_id)
             return
         except Exception:
-            print("Dataset does not have an uploaded v3.0 version. Continuing with conversion.")
+            logger.info("Dataset does not have an uploaded v3.0 version. Continuing with conversion.")
 
     # Set root based on whether local dataset path is provided
     use_local_dataset = False
@@ -488,7 +490,7 @@ def convert_dataset(
     if root.exists():
         validate_local_dataset_version(root)
         use_local_dataset = True
-        print(f"Using local dataset at {root}")
+        logger.info(f"Using local dataset at {root}")
 
     old_root = root.parent / f"{root.name}_old"
     new_root = root.parent / f"{root.name}_v30"
@@ -523,7 +525,7 @@ def convert_dataset(
         try:
             hub_api.delete_tag(repo_id, tag=CODEBASE_VERSION, repo_type="dataset")
         except (HTTPError, RevisionNotFoundError) as e:
-            print(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})")
+            logger.warning(f"tag={CODEBASE_VERSION} probably doesn't exist. Skipping exception ({e})")
             pass
         hub_api.delete_files(
             delete_patterns=["data/chunk*/episode_*", "meta/*.jsonl", "videos/chunk*"],

src/lerobot/scripts/lerobot_annotate.py

@@ -154,14 +154,14 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
     repo_id = cfg.new_repo_id or cfg.repo_id
     commit_message = cfg.push_commit_message or "Add steerable annotations (lerobot-annotate)"
     api = HfApi()
-    print(f"[lerobot-annotate] creating/locating dataset repo {repo_id}...", flush=True)
+    logger.info(f"[lerobot-annotate] creating/locating dataset repo {repo_id}...")
     api.create_repo(
         repo_id=repo_id,
         repo_type="dataset",
         private=cfg.push_private,
         exist_ok=True,
     )
-    print(f"[lerobot-annotate] uploading {root} -> {repo_id}...", flush=True)
+    logger.info(f"[lerobot-annotate] uploading {root} -> {repo_id}...")
     commit_info = api.upload_folder(
         folder_path=str(root),
         repo_id=repo_id,
@@ -172,7 +172,7 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
         # at the source dataset; a fresh card is generated below instead.
         ignore_patterns=[".annotate_staging/**", "**/.DS_Store", "README.md"],
     )
-    print(f"[lerobot-annotate] uploaded to https://huggingface.co/datasets/{repo_id}", flush=True)
+    logger.info(f"[lerobot-annotate] uploaded to https://huggingface.co/datasets/{repo_id}")
 
     dataset_info = load_info(root)
     card = create_lerobot_dataset_card(dataset_info=dataset_info, license="apache-2.0", repo_id=repo_id)
@@ -200,14 +200,13 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
         with suppress(RevisionNotFoundError):
             api.delete_tag(repo_id, tag=version_tag, repo_type="dataset")
         api.create_tag(**tag_kwargs)
-        print(f"[lerobot-annotate] tagged {repo_id} as {version_tag}", flush=True)
+        logger.info(f"[lerobot-annotate] tagged {repo_id} as {version_tag}")
     except Exception as exc:  # noqa: BLE001
-        print(
+        logger.warning(
             f"[lerobot-annotate] WARNING: could not create tag {version_tag!r} on {repo_id}: {exc}. "
             "Dataset is uploaded but ``LeRobotDataset`` won't be able to load it until it's tagged. "
             "Run: from huggingface_hub import HfApi; "
-            f"HfApi().create_tag({repo_id!r}, tag={version_tag!r}, repo_type='dataset', exist_ok=True)",
-            flush=True,
+            f"HfApi().create_tag({repo_id!r}, tag={version_tag!r}, repo_type='dataset', exist_ok=True)"
         )
 
 

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -89,6 +89,8 @@ from lerobot.datasets import LeRobotDataset
 from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD, SUCCESS
 from lerobot.utils.utils import init_logging
 
+logger = logging.getLogger(__name__)
+
 DEFAULT_FOXGLOVE_PORT = 8765
 DEFAULT_RERUN_PORT = 9090
 
@@ -299,7 +301,7 @@ def visualize_dataset(
             while True:
                 time.sleep(1)
         except KeyboardInterrupt:
-            print("Ctrl-C received. Exiting.")
+            logger.info("Ctrl-C received. Exiting.")
 
 
 def main():

src/lerobot/scripts/lerobot_eval.py

@@ -62,7 +62,7 @@ from dataclasses import asdict
 from functools import partial
 from pathlib import Path
 from pprint import pformat
-from typing import Any, TypedDict
+from typing import TYPE_CHECKING, Any, TypedDict
 
 import einops
 import gymnasium as gym
@@ -87,7 +87,7 @@ from lerobot.processor import PolicyProcessorPipeline
 from lerobot.types import PolicyAction
 from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_IMAGES, OBS_STR, REWARD
 from lerobot.utils.device_utils import get_safe_torch_device
-from lerobot.utils.import_utils import register_third_party_plugins
+from lerobot.utils.import_utils import _peft_available, register_third_party_plugins, require_package
 from lerobot.utils.io_utils import write_video
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
@@ -95,6 +95,14 @@ from lerobot.utils.utils import (
     inside_slurm,
 )
 
+if TYPE_CHECKING or _peft_available:
+    from peft import PeftModel
+else:
+    PeftModel = None
+
+
+logger = logging.getLogger(__name__)
+
 
 def _env_features_to_dataset_features(env_features: dict) -> dict:
     """Convert EnvConfig.features to the dict format expected by LeRobotDataset.create()."""
@@ -444,13 +452,11 @@ def eval_policy(
         exc = ValueError(
             f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
         )
-        try:
-            from peft import PeftModel
-
-            if not isinstance(policy, PeftModel):
-                raise exc
-        except ImportError:
-            raise exc from None
+        if not _peft_available:
+            raise exc
+        require_package("peft", extra="peft")
+        if not isinstance(policy, PeftModel):
+            raise exc
 
     start = time.time()
     # Preserve the mode for direct callers. eval_policy_all scopes the mode
@@ -558,7 +564,7 @@ def eval_policy(
         if seeds:
             all_seeds.extend(seeds)
         else:
-            all_seeds.append(None)
+            all_seeds.extend([None] * env.num_envs)
 
         # FIXME: episode_data is either None or it doesn't exist
         if return_episode_data:
@@ -796,13 +802,13 @@ def eval_main(cfg: EvalPipelineConfig):
             recording_repo_id=cfg.eval.recording_repo_id,
             recording_private=cfg.eval.recording_private,
         )
-        print("Overall Aggregated Metrics:")
-        print(info["overall"])
+        logger.info("Overall Aggregated Metrics:")
+        logger.info(info["overall"])
 
         # Print per-suite stats
         for task_group, task_group_info in info.items():
-            print(f"\nAggregated Metrics for {task_group}:")
-            print(task_group_info)
+            logger.info(f"\nAggregated Metrics for {task_group}:")
+            logger.info(task_group_info)
     # Close all vec envs
     close_envs(envs)
 

src/lerobot/scripts/lerobot_find_cameras.py

@@ -40,6 +40,7 @@ from PIL import Image
 from lerobot.cameras import ColorMode
 from lerobot.cameras.opencv import OpenCVCamera, OpenCVCameraConfig
 from lerobot.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
+from lerobot.utils.utils import init_logging
 
 logger = logging.getLogger(__name__)
 
@@ -285,6 +286,8 @@ def save_images_from_all_cameras(
 
 
 def main():
+    init_logging()
+
     parser = argparse.ArgumentParser(
         description="Unified camera utility script for listing cameras and capturing images."
     )

src/lerobot/scripts/lerobot_replay.py

@@ -61,6 +61,7 @@ from lerobot.robots import (  # noqa: F401
     earthrover_mini_plus,
     hope_jr,
     koch_follower,
+    lekiwi,
     make_robot_from_config,
     omx_follower,
     openarm_follower,

src/lerobot/scripts/lerobot_rollout.py

@@ -165,6 +165,7 @@ from lerobot.robots import (  # noqa: F401
     earthrover_mini_plus,
     hope_jr,
     koch_follower,
+    lekiwi,
     omx_follower,
     openarm_follower,
     reachy2,

src/lerobot/scripts/lerobot_train.py

@@ -22,7 +22,8 @@ import dataclasses
 import logging
 import sys
 import time
-from contextlib import nullcontext
+from collections.abc import Iterator
+from contextlib import contextmanager, nullcontext
 from pprint import pformat
 from typing import TYPE_CHECKING, Any
 
@@ -57,7 +58,7 @@ from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.rewards import make_reward_pre_post_processors
 from lerobot.utils.collate import lerobot_collate_fn
-from lerobot.utils.import_utils import register_third_party_plugins
+from lerobot.utils.import_utils import _peft_available, register_third_party_plugins, require_package
 from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
@@ -68,9 +69,38 @@ from lerobot.utils.utils import (
     inside_slurm,
 )
 
+if TYPE_CHECKING or _peft_available:
+    from peft import PeftModel
+else:
+    PeftModel = None
+
 from .lerobot_eval import eval_policy_all
 
 
+@contextmanager
+def _make_eval_envs(cfg: TrainPipelineConfig) -> Iterator[dict[str, dict[int, Any]]]:
+    """Create evaluation environments for one run and always dispose of them."""
+    envs = make_env(
+        cfg.env,
+        n_envs=cfg.eval.batch_size,
+        use_async_envs=cfg.eval.use_async_envs,
+    )
+    try:
+        yield envs
+    finally:
+        close_envs(envs)
+
+
+def _dataloader_worker_kwargs(cfg: TrainPipelineConfig) -> dict[str, Any]:
+    """Return worker-only DataLoader options, disabling them for single-process loading."""
+    workers_enabled = cfg.num_workers > 0
+    return {
+        "prefetch_factor": cfg.prefetch_factor if workers_enabled else None,
+        "persistent_workers": cfg.persistent_workers and workers_enabled,
+        "multiprocessing_context": cfg.dataloader_multiprocessing_context if workers_enabled else None,
+    }
+
+
 def update_policy(
     train_metrics: MetricsTracker,
     policy: PreTrainedPolicy,
@@ -197,8 +227,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     if cfg.job.is_remote:
         return submit_to_hf(cfg)
 
-    from lerobot.utils.import_utils import require_package
-
     require_package("accelerate", extra="training")
     from accelerate import Accelerator
     from accelerate.utils import DistributedDataParallelKwargs, DistributedType
@@ -267,14 +295,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     if not is_main_process:
         dataset, eval_dataset = make_train_eval_datasets(cfg)
 
-    # Create environment used for evaluating checkpoints during training on simulation data.
-    # On real-world data, no need to create an environment as evaluations are done outside train.py,
-    # using the eval.py instead, with gym_dora environment and dora-rs.
-    eval_env = None
-    if cfg.env_eval_freq > 0 and cfg.env is not None and is_main_process:
-        logging.info("Creating env")
-        eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
-
     if cfg.is_reward_model_training:
         if is_main_process:
             logging.info("Creating reward model")
@@ -302,7 +322,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     if cfg.peft is not None:
         if cfg.is_reward_model_training:
             raise ValueError("PEFT is only supported for policy training. ")
-        from peft import PeftModel
+        require_package("peft", extra="peft")
 
         if isinstance(policy, PeftModel):
             logging.info("PEFT adapter already loaded from checkpoint, skipping wrap_with_peft.")
@@ -473,8 +493,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         pin_memory=device.type == "cuda",
         drop_last=False,
         collate_fn=collate_fn,
-        prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
-        persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
+        **_dataloader_worker_kwargs(cfg),
     )
 
     # Build eval dataloader if a held-out split exists
@@ -500,8 +519,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             pin_memory=device.type == "cuda",
             drop_last=False,
             collate_fn=eval_collate_fn,
-            prefetch_factor=cfg.prefetch_factor if cfg.num_workers > 0 else None,
-            persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
+            **_dataloader_worker_kwargs(cfg),
         )
 
     # Prepare everything with accelerator
@@ -684,7 +702,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             if is_main_process:
                 step_id = get_step_identifier(step, cfg.steps)
                 logging.info(f"Eval policy at step {step}")
-                with torch.no_grad(), accelerator.autocast():
+                with _make_eval_envs(cfg) as eval_env, torch.no_grad(), accelerator.autocast():
                     eval_info = eval_policy_all(
                         envs=eval_env,  # dict[suite][task_id] -> vec_env
                         policy=accelerator.unwrap_model(policy),
@@ -732,9 +750,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     if is_main_process:
         progbar.close()
 
-    if eval_env:
-        close_envs(eval_env)
-
     is_fsdp = accelerator.distributed_type == DistributedType.FSDP
     model_state_dict = accelerator.get_state_dict(policy) if is_fsdp else None
     if is_main_process:

src/lerobot/scripts/lerobot_train_tokenizer.py

@@ -45,6 +45,7 @@ lerobot-train-tokenizer \
 """
 
 import json
+import logging
 from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING
@@ -63,6 +64,9 @@ else:
 from lerobot.configs import NormalizationMode, parser
 from lerobot.datasets import LeRobotDataset
 from lerobot.utils.constants import ACTION, OBS_STATE
+from lerobot.utils.utils import init_logging
+
+logger = logging.getLogger(__name__)
 
 
 @dataclass
@@ -274,11 +278,8 @@ def process_episode(args):
 
         return action_chunks
 
-    except Exception as e:
-        print(f"Error processing episode {ep_idx}: {e}")
-        import traceback
-
-        traceback.print_exc()
+    except Exception:
+        logger.exception("Error processing episode %s", ep_idx)
         return None
 
 
@@ -300,10 +301,10 @@ def train_fast_tokenizer(
     Returns:
         Trained FAST tokenizer
     """
-    print(f"Training FAST tokenizer on {len(action_chunks)} action chunks...")
-    print(f"Action chunk shape: {action_chunks.shape}")
-    print(f"Vocab size: {vocab_size}")
-    print(f"DCT scale: {scale}")
+    logger.info(f"Training FAST tokenizer on {len(action_chunks)} action chunks...")
+    logger.info(f"Action chunk shape: {action_chunks.shape}")
+    logger.info(f"Vocab size: {vocab_size}")
+    logger.info(f"DCT scale: {scale}")
 
     # download the tokenizer source code (not pretrained weights)
     # we'll train a new tokenizer on our own data
@@ -314,7 +315,7 @@ def train_fast_tokenizer(
 
     # train the new tokenizer on our action data using .fit()
     # this trains the BPE tokenizer on DCT coefficients
-    print("Training new tokenizer (this may take a few minutes)...")
+    logger.info("Training new tokenizer (this may take a few minutes)...")
     tokenizer = base_tokenizer.fit(
         action_data_list,
         scale=scale,
@@ -322,21 +323,21 @@ def train_fast_tokenizer(
         time_horizon=action_chunks.shape[1],  # action_horizon
         action_dim=action_chunks.shape[2],  # encoded dimensions
     )
-    print("✓ Tokenizer training complete!")
+    logger.info("✓ Tokenizer training complete!")
 
     # validate it works
     sample_chunk = action_chunks[0]
     encoded = tokenizer(sample_chunk[None])[0]
     if isinstance(encoded, list):
         encoded = np.array(encoded)
-    print(f"Sample encoding: {len(encoded)} tokens for chunk shape {sample_chunk.shape}")
+    logger.info(f"Sample encoding: {len(encoded)} tokens for chunk shape {sample_chunk.shape}")
 
     return tokenizer
 
 
 def compute_compression_stats(tokenizer, action_chunks: np.ndarray):
     """Compute compression statistics."""
-    print("\nComputing compression statistics...")
+    logger.info("\nComputing compression statistics...")
 
     # sample for stats (use max 1000 chunks for speed)
     sample_size = min(1000, len(action_chunks))
@@ -366,12 +367,12 @@ def compute_compression_stats(tokenizer, action_chunks: np.ndarray):
         "max_token_length": float(np.max(token_lengths)),
     }
 
-    print("Compression Statistics:")
-    print(f"  Average compression ratio: {stats['compression_ratio']:.2f}x")
-    print(f"  Mean token length: {stats['mean_token_length']:.1f}")
-    print(f"  P99 token length: {stats['p99_token_length']:.0f}")
-    print(f"  Min token length: {stats['min_token_length']:.0f}")
-    print(f"  Max token length: {stats['max_token_length']:.0f}")
+    logger.info("Compression Statistics:")
+    logger.info(f"  Average compression ratio: {stats['compression_ratio']:.2f}x")
+    logger.info(f"  Mean token length: {stats['mean_token_length']:.1f}")
+    logger.info(f"  P99 token length: {stats['p99_token_length']:.0f}")
+    logger.info(f"  Min token length: {stats['min_token_length']:.0f}")
+    logger.info(f"  Max token length: {stats['max_token_length']:.0f}")
 
     return stats
 
@@ -385,9 +386,9 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
         cfg: TokenizerTrainingConfig dataclass with all configuration parameters
     """
     # load dataset
-    print(f"Loading dataset: {cfg.repo_id}")
+    logger.info(f"Loading dataset: {cfg.repo_id}")
     dataset = LeRobotDataset(repo_id=cfg.repo_id, root=cfg.root)
-    print(f"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+    logger.info(f"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
 
     # parse normalization mode
     try:
@@ -397,7 +398,7 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
             f"Invalid normalization_mode: {cfg.normalization_mode}. "
             f"Must be one of: {', '.join([m.value for m in NormalizationMode])}"
         ) from err
-    print(f"Normalization mode: {norm_mode.value}")
+    logger.info(f"Normalization mode: {norm_mode.value}")
 
     # parse encoded dimensions
     encoded_dim_ranges = []
@@ -406,38 +407,38 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
         encoded_dim_ranges.append((start, end))
 
     total_encoded_dims = sum(end - start for start, end in encoded_dim_ranges)
-    print(f"Encoding {total_encoded_dims} dimensions: {cfg.encoded_dims}")
+    logger.info(f"Encoding {total_encoded_dims} dimensions: {cfg.encoded_dims}")
 
     # parse relative dimensions
     relative_dim_list = None
     if cfg.relative_dims is not None and cfg.relative_dims.strip():
         relative_dim_list = [int(d.strip()) for d in cfg.relative_dims.split(",")]
-        print(f"Relative dimensions: {relative_dim_list}")
+        logger.info(f"Relative dimensions: {relative_dim_list}")
     else:
-        print("No relative dimensions specified")
+        logger.info("No relative dimensions specified")
 
-    print(f"Use relative transform: {cfg.use_relative_transform}")
+    logger.info(f"Use relative transform: {cfg.use_relative_transform}")
     if cfg.use_relative_transform and (relative_dim_list is None or len(relative_dim_list) == 0):
-        print(
+        logger.warning(
             "Warning: use_relative_transform=True but no relative_dims specified. "
             "No relative transform will be applied."
         )
 
-    print(f"Action horizon: {cfg.action_horizon}")
-    print(f"State key: {cfg.state_key}")
+    logger.info(f"Action horizon: {cfg.action_horizon}")
+    logger.info(f"State key: {cfg.state_key}")
 
     # determine episodes to process
     num_episodes = dataset.num_episodes
     if cfg.max_episodes is not None:
         num_episodes = min(cfg.max_episodes, num_episodes)
 
-    print(f"Processing {num_episodes} episodes...")
+    logger.info(f"Processing {num_episodes} episodes...")
 
     # process episodes sequentially (to avoid pickling issues with dataset)
     all_chunks = []
     for ep_idx in range(num_episodes):
         if ep_idx % 10 == 0:
-            print(f"  Processing episode {ep_idx}/{num_episodes}...")
+            logger.info(f"  Processing episode {ep_idx}/{num_episodes}...")
 
         chunks = process_episode(
             (
@@ -455,19 +456,19 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
 
     # concatenate all chunks
     all_chunks = np.concatenate(all_chunks, axis=0)
-    print(f"Collected {len(all_chunks)} action chunks")
+    logger.info(f"Collected {len(all_chunks)} action chunks")
 
     # extract only encoded dimensions FIRST (before normalization)
     encoded_chunks = []
     for start, end in encoded_dim_ranges:
         encoded_chunks.append(all_chunks[:, :, start:end])
     encoded_chunks = np.concatenate(encoded_chunks, axis=-1)  # [N, H, D_encoded]
-    print(f"Extracted {encoded_chunks.shape[-1]} encoded dimensions")
+    logger.info(f"Extracted {encoded_chunks.shape[-1]} encoded dimensions")
 
     # apply normalization to encoded dimensions
-    print("\nBefore normalization - overall stats:")
-    print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
-    print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+    logger.info("\nBefore normalization - overall stats:")
+    logger.info(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
+    logger.info(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
 
     # get normalization stats from dataset
     norm_stats = dataset.meta.stats
@@ -489,9 +490,9 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
                     encoded_stats[stat_name] = stat_array[encoded_dim_indices]
 
         if encoded_stats:
-            print(f"\nNormalization stats for encoded dimensions (mode: {norm_mode.value}):")
+            logger.info(f"\nNormalization stats for encoded dimensions (mode: {norm_mode.value}):")
             for stat_name, stat_values in encoded_stats.items():
-                print(
+                logger.info(
                     f"  {stat_name}: shape={stat_values.shape}, "
                     f"range=[{np.min(stat_values):.4f}, {np.max(stat_values):.4f}]"
                 )
@@ -499,27 +500,27 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
             # apply normalization based on mode
             try:
                 encoded_chunks = apply_normalization(encoded_chunks, encoded_stats, norm_mode, eps=1e-8)
-                print(f"\nApplied {norm_mode.value} normalization")
+                logger.info(f"\nApplied {norm_mode.value} normalization")
             except ValueError as e:
-                print(f"Warning: {e}. Using raw actions without normalization.")
+                logger.warning(f"Warning: {e}. Using raw actions without normalization.")
 
-            print("\nAfter normalization - overall stats:")
-            print(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
-            print(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
+            logger.info("\nAfter normalization - overall stats:")
+            logger.info(f"  Min: {np.min(encoded_chunks):.4f}, Max: {np.max(encoded_chunks):.4f}")
+            logger.info(f"  Mean: {np.mean(encoded_chunks):.4f}, Std: {np.std(encoded_chunks):.4f}")
 
-            print("\nPer-dimension stats (after normalization):")
+            logger.info("\nPer-dimension stats (after normalization):")
             for d in range(encoded_chunks.shape[-1]):
                 dim_data = encoded_chunks[:, :, d]
-                print(
+                logger.info(
                     f"  Dim {d}: min={np.min(dim_data):7.4f}, max={np.max(dim_data):7.4f}, "
                     f"mean={np.mean(dim_data):7.4f}, std={np.std(dim_data):7.4f}"
                 )
         else:
-            print("Warning: Could not extract stats for encoded dimensions, using raw actions")
+            logger.warning("Warning: Could not extract stats for encoded dimensions, using raw actions")
     else:
-        print("Warning: No normalization stats found in dataset, using raw actions")
+        logger.warning("Warning: No normalization stats found in dataset, using raw actions")
 
-    print(f"Encoded chunks shape: {encoded_chunks.shape}")
+    logger.info(f"Encoded chunks shape: {encoded_chunks.shape}")
 
     # train FAST tokenizer
     tokenizer = train_fast_tokenizer(
@@ -561,8 +562,8 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
     with open(output_path / "metadata.json", "w") as f:
         json.dump(metadata, f, indent=2)
 
-    print(f"\nSaved FAST tokenizer to {output_path}")
-    print(f"Metadata: {json.dumps(metadata, indent=2)}")
+    logger.info(f"\nSaved FAST tokenizer to {output_path}")
+    logger.info(f"Metadata: {json.dumps(metadata, indent=2)}")
 
     # push to Hugging Face Hub if requested
     if cfg.push_to_hub:
@@ -570,10 +571,10 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
         hub_repo_id = cfg.hub_repo_id
         if hub_repo_id is None:
             hub_repo_id = output_path.name
-            print(f"\nNo hub_repo_id provided, using: {hub_repo_id}")
+            logger.info(f"\nNo hub_repo_id provided, using: {hub_repo_id}")
 
-        print(f"\nPushing tokenizer to Hugging Face Hub: {hub_repo_id}")
-        print(f"   Private: {cfg.hub_private}")
+        logger.info(f"\nPushing tokenizer to Hugging Face Hub: {hub_repo_id}")
+        logger.info(f"   Private: {cfg.hub_private}")
 
         try:
             # use the tokenizer's push_to_hub method
@@ -593,14 +594,15 @@ def train_tokenizer(cfg: TokenizerTrainingConfig):
                 commit_message="Upload tokenizer metadata",
             )
 
-            print(f"Successfully pushed tokenizer to: https://huggingface.co/{hub_repo_id}")
+            logger.info(f"Successfully pushed tokenizer to: https://huggingface.co/{hub_repo_id}")
         except Exception as e:
-            print(f"Error pushing to hub: {e}")
-            print("   Make sure you're logged in with `huggingface-cli login`")
+            logger.error(f"Error pushing to hub: {e}")
+            logger.error("   Make sure you're logged in with `huggingface-cli login`")
 
 
 def main():
     """CLI entry point that parses arguments and runs the tokenizer training."""
+    init_logging()
     train_tokenizer()
 
 

src/lerobot/transforms/transforms.py

@@ -41,7 +41,7 @@ class RandomSubsetApply(Transform):
 
     def __init__(
         self,
-        transforms: Sequence[Callable],
+        transforms: Sequence[Callable[..., Any]],
         p: list[float] | None = None,
         n_subset: int | None = None,
         random_order: bool = False,
@@ -50,7 +50,7 @@ class RandomSubsetApply(Transform):
         if not isinstance(transforms, Sequence):
             raise TypeError("Argument transforms should be a sequence of callables")
         if p is None:
-            p = [1] * len(transforms)
+            p = [1.0] * len(transforms)
         elif len(p) != len(transforms):
             raise ValueError(
                 f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
@@ -69,7 +69,7 @@ class RandomSubsetApply(Transform):
         self.n_subset = n_subset
         self.random_order = random_order
 
-        self.selected_transforms = None
+        self.selected_transforms: list[Callable[..., Any]] = []
 
     def forward(self, *inputs: Any) -> Any:
         needs_unpacking = len(inputs) > 1
@@ -119,7 +119,7 @@ class SharpnessJitter(Transform):
         super().__init__()
         self.sharpness = self._check_input(sharpness)
 
-    def _check_input(self, sharpness):
+    def _check_input(self, sharpness: float | Sequence[float]) -> tuple[float, float]:
         if isinstance(sharpness, (int | float)):
             if sharpness < 0:
                 raise ValueError("If sharpness is a single number, it must be non negative.")
@@ -215,7 +215,7 @@ class ImageTransformsConfig:
     )
 
 
-def make_transform_from_config(cfg: ImageTransformConfig):
+def make_transform_from_config(cfg: ImageTransformConfig) -> Transform:
     if cfg.type == "SharpnessJitter":
         return SharpnessJitter(**cfg.kwargs)
 
@@ -236,8 +236,8 @@ class ImageTransforms(Transform):
         super().__init__()
         self._cfg = cfg
 
-        self.weights = []
-        self.transforms = {}
+        self.weights: list[float] = []
+        self.transforms: dict[str, Transform] = {}
         for tf_name, tf_cfg in cfg.tfs.items():
             if tf_cfg.weight <= 0.0:
                 continue

src/lerobot/utils/process.py

@@ -16,11 +16,39 @@
 # limitations under the License.
 
 import logging
+import multiprocessing
 import os
 import signal
 import sys
 
 
+def ensure_multiprocessing_start_method(start_method: str | None) -> None:
+    """Set a multiprocessing start method once, or verify the existing method matches.
+
+    Passing ``None`` leaves Python's process-wide default untouched. This is useful
+    when LeRobot is embedded in an application that owns multiprocessing setup.
+    """
+    if start_method is None:
+        return
+
+    available_methods = multiprocessing.get_all_start_methods()
+    if start_method not in available_methods:
+        raise ValueError(
+            f"Multiprocessing start method must be one of {available_methods} on this platform, "
+            f"got {start_method!r}."
+        )
+
+    current_method = multiprocessing.get_start_method(allow_none=True)
+    if current_method is None:
+        multiprocessing.set_start_method(start_method)
+    elif current_method != start_method:
+        raise RuntimeError(
+            f"Multiprocessing start method is already {current_method!r}; cannot change it to "
+            f"{start_method!r}. Set the configured multiprocessing context to null to keep the "
+            "application's existing method, or launch LeRobot in a fresh process."
+        )
+
+
 class ProcessSignalHandler:
     """Utility class to attach graceful shutdown signal handlers.
 

src/lerobot/utils/utils.py

@@ -133,10 +133,13 @@ def say(text: str, blocking: bool = False):
     else:
         raise RuntimeError("Unsupported operating system for text-to-speech.")
 
-    if blocking:
-        subprocess.run(cmd, check=True)
-    else:
-        subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0)
+    try:
+        if blocking:
+            subprocess.run(cmd, check=True, timeout=5)
+        else:
+            subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0)
+    except (FileNotFoundError, subprocess.TimeoutExpired) as e:
+        logging.warning("Text-to-speech command failed: %s | Error: %s", cmd, e)
 
 
 def log_say(text: str, play_sounds: bool = True, blocking: bool = False):

tests/cameras/test_opencv.py

@@ -20,7 +20,7 @@
 # ```
 
 from pathlib import Path
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
 import cv2
 import numpy as np
@@ -123,6 +123,73 @@ def test_invalid_width_connect():
         camera.connect(warmup=False)
 
 
+def test_connect_cleans_up_after_settings_failure_and_allows_retry():
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0)
+    camera = OpenCVCamera(config)
+    opened_captures = []
+
+    def fail_settings():
+        opened_captures.append(camera.videocapture)
+        raise RuntimeError("settings failed")
+
+    with (
+        patch.object(camera, "_configure_capture_settings", side_effect=fail_settings),
+        pytest.raises(RuntimeError, match="settings failed"),
+    ):
+        camera.connect(warmup=False)
+
+    assert camera.videocapture is None
+    assert camera.thread is None
+    assert not camera.is_connected
+    assert opened_captures[0] is not None
+    assert not opened_captures[0].isOpened()
+
+    camera.connect(warmup=False)
+    assert camera.is_connected
+    camera.disconnect()
+
+
+def test_connect_cleans_up_after_warmup_failure_and_allows_retry():
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=1)
+    camera = OpenCVCamera(config)
+    read_threads = []
+
+    def fail_warmup(*_args, **_kwargs):
+        read_threads.append(camera.thread)
+        raise TimeoutError("no frame")
+
+    with (
+        patch.object(camera, "async_read", side_effect=fail_warmup),
+        pytest.raises(TimeoutError, match="no frame"),
+    ):
+        camera.connect()
+
+    assert camera.videocapture is None
+    assert camera.thread is None
+    assert not camera.is_connected
+    assert read_threads[0] is not None
+    assert not read_threads[0].is_alive()
+
+    camera.connect(warmup=False)
+    assert camera.is_connected
+    camera.disconnect()
+
+
+def test_find_cameras_releases_unopened_handles():
+    module_path = OpenCVCamera.__module__
+    unopened_capture = MagicMock()
+    unopened_capture.isOpened.return_value = False
+
+    with (
+        patch(f"{module_path}.platform.system", return_value="Darwin"),
+        patch(f"{module_path}.MAX_OPENCV_INDEX", 1),
+        patch(f"{module_path}.cv2.VideoCapture", return_value=unopened_capture),
+    ):
+        assert OpenCVCamera.find_cameras() == []
+
+    unopened_capture.release.assert_called_once_with()
+
+
 @pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES)
 def test_read(index_or_path):
     config = OpenCVCameraConfig(index_or_path=index_or_path, warmup_s=0)

tests/cameras/test_realsense.py

@@ -20,7 +20,7 @@
 # ```
 
 from pathlib import Path
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
 import numpy as np
 import pytest
@@ -30,6 +30,8 @@ from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnected
 
 pytest.importorskip("pyrealsense2")
 
+import pyrealsense2 as rs
+
 from lerobot.cameras.realsense import RealSenseCamera, RealSenseCameraConfig
 
 TEST_ARTIFACTS_DIR = Path(__file__).parent.parent / "artifacts" / "cameras"
@@ -61,6 +63,17 @@ def test_abc_implementation():
     _ = RealSenseCamera(config)
 
 
+@pytest.mark.parametrize("option", ["exposure", "gain", "white_balance"])
+def test_manual_color_option_requires_rgb(option):
+    with pytest.raises(ValueError, match="use_rgb=True"):
+        RealSenseCameraConfig(
+            serial_number_or_name="042",
+            use_rgb=False,
+            use_depth=True,
+            **{option: 100},
+        )
+
+
 def test_connect():
     config = RealSenseCameraConfig(serial_number_or_name="042", warmup_s=0)
 
@@ -83,6 +96,27 @@ def test_connect_invalid_camera_path(patch_realsense):
         camera.connect(warmup=False)
 
 
+def test_connect_cleans_up_when_sensor_configuration_fails():
+    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=120)
+    camera = RealSenseCamera(config)
+    pipeline = MagicMock()
+    pipeline.start.return_value = MagicMock()
+
+    with (
+        patch("lerobot.cameras.realsense.camera_realsense.rs.pipeline", return_value=pipeline),
+        patch.object(camera, "_configure_rs_pipeline_config"),
+        patch.object(camera, "_configure_capture_settings"),
+        patch.object(camera, "_configure_sensor_options", side_effect=ValueError("invalid exposure")),
+        pytest.raises(ValueError, match="invalid exposure"),
+    ):
+        camera.connect(warmup=False)
+
+    pipeline.stop.assert_called_once_with()
+    assert camera.rs_pipeline is None
+    assert camera.rs_profile is None
+    assert not camera.is_connected
+
+
 def test_invalid_width_connect():
     config = RealSenseCameraConfig(serial_number_or_name="042", width=99999, height=480, fps=30)
     camera = RealSenseCamera(config)
@@ -91,6 +125,33 @@ def test_invalid_width_connect():
         camera.connect(warmup=False)
 
 
+def test_connect_cleans_up_after_warmup_failure_and_allows_retry():
+    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30)
+    camera = RealSenseCamera(config)
+    read_threads = []
+
+    def fail_warmup(*_args, **_kwargs):
+        read_threads.append(camera.thread)
+        raise TimeoutError("no frame")
+
+    with (
+        patch.object(camera, "async_read", side_effect=fail_warmup),
+        pytest.raises(TimeoutError, match="no frame"),
+    ):
+        camera.connect()
+
+    assert camera.rs_pipeline is None
+    assert camera.rs_profile is None
+    assert camera.thread is None
+    assert not camera.is_connected
+    assert read_threads[0] is not None
+    assert not read_threads[0].is_alive()
+
+    camera.connect(warmup=False)
+    assert camera.is_connected
+    camera.disconnect()
+
+
 def test_read():
     config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0)
     with RealSenseCamera(config) as camera:
@@ -228,6 +289,203 @@ def test_read_latest_too_old():
             _ = camera.read_latest(max_age_ms=0)  # immediately too old
 
 
+def _make_mock_sensor(name: str, supported_options: set | None = None) -> MagicMock:
+    """Build a fake rs.sensor that reports a name and a configurable supported-options set."""
+    supported = supported_options if supported_options is not None else set()
+    sensor = MagicMock()
+    sensor.get_info.return_value = name
+    sensor.supports.side_effect = lambda opt: opt in supported
+    return sensor
+
+
+def _attach_mock_color_sensor(camera: RealSenseCamera, sensor: MagicMock) -> None:
+    """Wire camera.rs_profile so _get_color_sensor finds the given sensor."""
+    profile = MagicMock()
+    device = MagicMock()
+    device.query_sensors.return_value = [sensor]
+    profile.get_device.return_value = device
+    camera.rs_profile = profile
+
+
+def test_get_color_sensor_prefers_rgb_camera():
+    config = RealSenseCameraConfig(serial_number_or_name="042")
+    camera = RealSenseCamera(config)
+
+    rgb = _make_mock_sensor("RGB Camera")
+    stereo = _make_mock_sensor("Stereo Module")
+    profile = MagicMock()
+    device = MagicMock()
+    device.query_sensors.return_value = [stereo, rgb]
+    profile.get_device.return_value = device
+    camera.rs_profile = profile
+
+    assert camera._get_color_sensor() is rgb
+
+
+def test_get_color_sensor_falls_back_to_stereo_module():
+    """D405 has no separate RGB module; color comes from Stereo Module."""
+    config = RealSenseCameraConfig(serial_number_or_name="042")
+    camera = RealSenseCamera(config)
+
+    stereo = _make_mock_sensor("Stereo Module")
+    _attach_mock_color_sensor(camera, stereo)
+
+    assert camera._get_color_sensor() is stereo
+
+
+def test_get_color_sensor_raises_with_available_sensors():
+    config = RealSenseCameraConfig(serial_number_or_name="042")
+    camera = RealSenseCamera(config)
+
+    other = _make_mock_sensor("Motion Module")
+    _attach_mock_color_sensor(camera, other)
+
+    with pytest.raises(RuntimeError, match="Motion Module"):
+        camera._get_color_sensor()
+
+
+def test_configure_sensor_options_skipped_when_none():
+    config = RealSenseCameraConfig(serial_number_or_name="042")
+    camera = RealSenseCamera(config)
+
+    with patch.object(RealSenseCamera, "_get_color_sensor") as mock_get:
+        camera._configure_sensor_options()
+        mock_get.assert_not_called()
+
+
+def test_configure_sensor_options_applies_all_values():
+    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=120, gain=64, white_balance=4600)
+    camera = RealSenseCamera(config)
+
+    sensor = _make_mock_sensor(
+        "RGB Camera",
+        supported_options={
+            rs.option.enable_auto_exposure,
+            rs.option.exposure,
+            rs.option.gain,
+            rs.option.enable_auto_white_balance,
+            rs.option.white_balance,
+        },
+    )
+    _attach_mock_color_sensor(camera, sensor)
+
+    camera._configure_sensor_options()
+
+    sensor.set_option.assert_any_call(rs.option.enable_auto_exposure, 0)
+    sensor.set_option.assert_any_call(rs.option.exposure, 120)
+    sensor.set_option.assert_any_call(rs.option.gain, 64)
+    sensor.set_option.assert_any_call(rs.option.enable_auto_white_balance, 0)
+    sensor.set_option.assert_any_call(rs.option.white_balance, 4600)
+
+
+@pytest.mark.parametrize(
+    ("config_field", "option", "label"),
+    [
+        ("exposure", rs.option.exposure, "exposure"),
+        ("gain", rs.option.gain, "gain"),
+        ("white_balance", rs.option.white_balance, "white balance"),
+    ],
+)
+def test_configure_sensor_options_raises_when_requested_option_is_unsupported(config_field, option, label):
+    config = RealSenseCameraConfig(serial_number_or_name="042", **{config_field: 100})
+    camera = RealSenseCamera(config)
+
+    sensor = _make_mock_sensor("RGB Camera", supported_options=set())
+    _attach_mock_color_sensor(camera, sensor)
+
+    with pytest.raises(ValueError, match=label):
+        camera._configure_sensor_options()
+
+    sensor.supports.assert_any_call(option)
+    sensor.set_option.assert_not_called()
+
+
+@pytest.mark.parametrize(
+    ("config_field", "option", "value"),
+    [
+        ("exposure", rs.option.exposure, 120),
+        ("gain", rs.option.gain, 64),
+    ],
+)
+def test_configure_sensor_options_exposure_or_gain_disables_auto_exposure(config_field, option, value):
+    """white_balance=None should not touch auto white balance."""
+    config = RealSenseCameraConfig(serial_number_or_name="042", **{config_field: value})
+    camera = RealSenseCamera(config)
+
+    sensor = _make_mock_sensor(
+        "RGB Camera",
+        supported_options={rs.option.enable_auto_exposure, option},
+    )
+    _attach_mock_color_sensor(camera, sensor)
+
+    camera._configure_sensor_options()
+
+    calls = [call.args for call in sensor.set_option.call_args_list]
+    assert (rs.option.enable_auto_exposure, 0) in calls
+    assert (option, value) in calls
+    for opt, _ in calls:
+        assert opt != rs.option.enable_auto_white_balance
+        assert opt != rs.option.white_balance
+
+
+def test_configure_sensor_options_warns_when_auto_exposure_control_is_unsupported(caplog):
+    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=120)
+    camera = RealSenseCamera(config)
+
+    sensor = _make_mock_sensor("RGB Camera", supported_options={rs.option.exposure})
+    _attach_mock_color_sensor(camera, sensor)
+
+    with caplog.at_level("WARNING"):
+        camera._configure_sensor_options()
+
+    sensor.set_option.assert_called_once_with(rs.option.exposure, 120)
+    assert "does not support disabling auto-exposure" in caplog.text
+
+
+def test_configure_sensor_options_warns_when_auto_white_balance_control_is_unsupported(caplog):
+    config = RealSenseCameraConfig(serial_number_or_name="042", white_balance=4600)
+    camera = RealSenseCamera(config)
+
+    sensor = _make_mock_sensor("RGB Camera", supported_options={rs.option.white_balance})
+    _attach_mock_color_sensor(camera, sensor)
+
+    with caplog.at_level("WARNING"):
+        camera._configure_sensor_options()
+
+    sensor.set_option.assert_called_once_with(rs.option.white_balance, 4600)
+    assert "does not support disabling auto white balance" in caplog.text
+
+
+def test_configure_sensor_options_out_of_range_raises_value_error():
+    """set_option errors should be re-raised as ValueError with range diagnostics."""
+    config = RealSenseCameraConfig(serial_number_or_name="042", exposure=999999)
+    camera = RealSenseCamera(config)
+
+    sensor = _make_mock_sensor(
+        "RGB Camera",
+        supported_options={rs.option.enable_auto_exposure, rs.option.exposure},
+    )
+
+    def fake_set_option(option, value):
+        if option == rs.option.exposure:
+            raise RuntimeError("value out of range")
+
+    sensor.set_option.side_effect = fake_set_option
+
+    option_range = MagicMock(min=1, max=10000, step=1, default=156)
+    sensor.get_option_range.return_value = option_range
+
+    _attach_mock_color_sensor(camera, sensor)
+
+    with pytest.raises(ValueError, match="exposure") as exc_info:
+        camera._configure_sensor_options()
+
+    msg = str(exc_info.value)
+    assert "999999" in msg
+    assert "min=1" in msg
+    assert "max=10000" in msg
+
+
 @pytest.mark.parametrize(
     "rotation",
     [

tests/datasets/test_compute_stats.py

@@ -687,26 +687,6 @@ def test_compute_episode_stats_string_features_skipped():
     assert "q01" in stats["action"]
 
 
-@pytest.mark.parametrize("shape", [(0,), (0, 2), (2, 0), (1, 0, 2)])
-def test_compute_episode_stats_zero_width_feature_skipped(shape):
-    """Features with any zero-width dimension carry no values and are skipped."""
-    episode_data = {
-        "action": np.random.normal(0, 1, (100, 5)).astype(np.float32),
-        "target": np.zeros((100, *shape), dtype=np.float32),
-    }
-    features = {
-        "action": {"dtype": "float32", "shape": (5,)},
-        "target": {"dtype": "float32", "shape": shape},
-    }
-
-    stats = compute_episode_stats(episode_data, features)
-
-    # Zero-width features are skipped, just like strings; non-empty features are unaffected.
-    assert "target" not in stats
-    assert "action" in stats
-    assert "q01" in stats["action"]
-
-
 def test_aggregate_feature_stats_with_quantiles():
     """Test aggregating feature stats that include quantiles."""
     stats_ft_list = [

tests/datasets/test_dataset_writer.py

@@ -27,7 +27,6 @@ pytest.importorskip("datasets", reason="datasets is required (install lerobot[da
 
 from lerobot.configs import VideoEncoderConfig
 from lerobot.datasets.dataset_writer import _encode_video_worker
-from lerobot.datasets.feature_utils import get_hf_features_from_features
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import DEFAULT_IMAGE_PATH
 from tests.fixtures.constants import DEFAULT_FPS, DUMMY_REPO_ID
@@ -190,36 +189,6 @@ def test_save_multiple_episodes(tmp_path):
     assert dataset.meta.total_frames == total_frames
 
 
-def test_save_episode_with_zero_width_feature(tmp_path):
-    """A one-dimensional empty numeric feature round-trips and has no statistics."""
-    features = {
-        **SIMPLE_FEATURES,
-        "target": {"dtype": "float32", "shape": (0,), "names": None},
-    }
-    root = tmp_path / "ds"
-    dataset = LeRobotDataset.create(repo_id=DUMMY_REPO_ID, fps=DEFAULT_FPS, features=features, root=root)
-    for _ in range(4):
-        dataset.add_frame(_make_frame(features))
-    dataset.save_episode()
-    dataset.finalize()
-
-    assert dataset.meta.total_episodes == 1
-    assert dataset.meta.total_frames == 4
-
-    reloaded = LeRobotDataset(repo_id=DUMMY_REPO_ID, root=root)
-    target = np.asarray(reloaded[0]["target"])
-    assert target.shape == (0,)
-    assert "target" not in (reloaded.meta.stats or {})
-
-
-@pytest.mark.parametrize("shape", [(0, 2), (2, 0), (1, 0, 2)])
-def test_multidimensional_zero_width_feature_rejected(shape):
-    features = {"target": {"dtype": "float32", "shape": shape, "names": None}}
-
-    with pytest.raises(ValueError, match="Multidimensional features with a zero-width dimension"):
-        get_hf_features_from_features(features)
-
-
 # ── clear / lifecycle ────────────────────────────────────────────────
 
 

tests/datasets/test_lerobot_dataset.py

@@ -482,6 +482,20 @@ def test_add_frame_works_in_write_mode(tmp_path):
 # ── Resume mode ──────────────────────────────────────────────────────
 
 
+def test_resume_freshly_created_empty_dataset(tmp_path):
+    """resume() accepts a local dataset created before any episode was recorded."""
+    root = tmp_path / "resume_empty_ds"
+    LeRobotDataset.create(repo_id=DUMMY_REPO_ID, fps=DEFAULT_FPS, features=SIMPLE_FEATURES, root=root)
+
+    resumed = LeRobotDataset.resume(repo_id=DUMMY_REPO_ID, root=root)
+
+    assert isinstance(resumed.writer, DatasetWriter)
+    assert resumed.meta.total_episodes == 0
+    assert resumed.meta.total_frames == 0
+    assert resumed.meta.tasks is None
+    assert resumed.meta.episodes is None
+
+
 def test_resume_creates_writer(tmp_path):
     """After resume(), writer is a DatasetWriter."""
     root = tmp_path / "resume_ds"

tests/policies/test_factory_peft_revision.py

@@ -0,0 +1,83 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+import torch
+
+import lerobot.policies.factory as policy_factory
+
+
+def test_make_policy_keeps_peft_adapter_and_base_revisions_separate(monkeypatch):
+    cfg = SimpleNamespace(
+        type="mock",
+        device="cpu",
+        pretrained_path="user/adapter",
+        pretrained_revision="adapter-sha",
+        use_peft=True,
+        input_features={},
+        output_features={},
+    )
+    dataset_meta = SimpleNamespace(features={}, stats={})
+
+    base_policy = torch.nn.Linear(1, 1)
+    policy_from_pretrained = MagicMock(return_value=base_policy)
+    policy_class = SimpleNamespace(from_pretrained=policy_from_pretrained)
+    monkeypatch.setattr(policy_factory, "get_policy_class", lambda _: policy_class)
+    monkeypatch.setattr(policy_factory, "dataset_to_policy_features", lambda _: {})
+    monkeypatch.setattr(policy_factory, "validate_visual_features_consistency", lambda *args: None)
+
+    peft_config = SimpleNamespace(
+        base_model_name_or_path="user/base-policy",
+        revision="base-sha",
+    )
+    peft_config_from_pretrained = MagicMock(return_value=peft_config)
+    adapted_policy = torch.nn.Linear(1, 1)
+    peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
+    require_package = MagicMock()
+    monkeypatch.setattr(policy_factory, "require_package", require_package)
+    monkeypatch.setattr(
+        policy_factory,
+        "PeftConfig",
+        SimpleNamespace(from_pretrained=peft_config_from_pretrained),
+    )
+    monkeypatch.setattr(
+        policy_factory,
+        "PeftModel",
+        SimpleNamespace(from_pretrained=peft_model_from_pretrained),
+    )
+
+    policy = policy_factory.make_policy(cfg, ds_meta=dataset_meta)
+
+    assert policy is adapted_policy
+    require_package.assert_called_once_with("peft", extra="peft")
+    peft_config_from_pretrained.assert_called_once_with(
+        "user/adapter",
+        revision="adapter-sha",
+    )
+    policy_from_pretrained.assert_called_once_with(
+        config=cfg,
+        dataset_stats=dataset_meta.stats,
+        dataset_meta=dataset_meta,
+        pretrained_name_or_path="user/base-policy",
+        revision="base-sha",
+    )
+    peft_model_from_pretrained.assert_called_once_with(
+        base_policy,
+        "user/adapter",
+        config=peft_config,
+        revision="adapter-sha",
+        is_trainable=True,
+    )

tests/policies/test_gaussian_actor_config.py

@@ -113,6 +113,7 @@ def test_gaussian_actor_config_default_initialization():
     # Concurrency configuration
     assert config.concurrency.actor == "threads"
     assert config.concurrency.learner == "threads"
+    assert config.concurrency.multiprocessing_context == "spawn"
 
     assert isinstance(config.actor_network_kwargs, ActorNetworkConfig)
     assert isinstance(config.policy_kwargs, PolicyConfig)
@@ -152,6 +153,7 @@ def test_concurrency_config():
     config = ConcurrencyConfig()
     assert config.actor == "threads"
     assert config.learner == "threads"
+    assert config.multiprocessing_context == "spawn"
 
 
 def test_gaussian_actor_config_custom_initialization():

tests/processor/test_pipeline_from_pretrained_helpers.py

@@ -26,8 +26,17 @@ import tempfile
 from pathlib import Path
 
 import pytest
+import torch
+from safetensors.torch import save_file
 
-from lerobot.processor.pipeline import DataProcessorPipeline, ProcessorMigrationError
+from lerobot.configs import PipelineFeatureType, PolicyFeature
+from lerobot.processor.pipeline import (
+    DataProcessorPipeline,
+    ProcessorMigrationError,
+    ProcessorStep,
+    ProcessorStepRegistry,
+)
+from lerobot.types import EnvTransition
 
 # Simplified Config Loading Tests
 
@@ -98,6 +107,140 @@ def test_load_config_nonexistent_path_tries_hub():
         DataProcessorPipeline._load_config("nonexistent/path", "processor.json", {})
 
 
+def test_from_pretrained_local_directory_missing_state_does_not_call_hub(monkeypatch):
+    """Local processor dirs must fail locally when a state file is missing."""
+
+    @ProcessorStepRegistry.register("local_missing_state_step")
+    class LocalMissingStateStep(ProcessorStep):
+        def __call__(self, transition: EnvTransition) -> EnvTransition:
+            return transition
+
+        def transform_features(
+            self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+        ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+            return features
+
+        def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+            pass
+
+    try:
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_path = Path(tmp_dir)
+            config = {
+                "name": "LocalMissingStatePipeline",
+                "steps": [{"registry_name": "local_missing_state_step", "state_file": "missing.safetensors"}],
+            }
+            (tmp_path / "processor.json").write_text(json.dumps(config))
+
+            def fail_hub_download(*args, **kwargs):
+                pytest.fail("local missing processor state should not call hf_hub_download")
+
+            monkeypatch.setattr("lerobot.processor.pipeline.hf_hub_download", fail_hub_download)
+
+            with pytest.raises(FileNotFoundError, match="missing.safetensors.*local processor pipeline"):
+                DataProcessorPipeline.from_pretrained(tmp_path, config_filename="processor.json")
+    finally:
+        ProcessorStepRegistry.unregister("local_missing_state_step")
+
+
+def test_from_pretrained_local_config_file_missing_state_does_not_call_hub(monkeypatch):
+    """Local single-file processor configs must also keep missing state resolution local."""
+
+    @ProcessorStepRegistry.register("local_file_missing_state_step")
+    class LocalFileMissingStateStep(ProcessorStep):
+        def __call__(self, transition: EnvTransition) -> EnvTransition:
+            return transition
+
+        def transform_features(
+            self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+        ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+            return features
+
+        def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+            pass
+
+    try:
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_path = Path(tmp_dir)
+            config_path = tmp_path / "processor.json"
+            config = {
+                "name": "LocalFileMissingStatePipeline",
+                "steps": [
+                    {"registry_name": "local_file_missing_state_step", "state_file": "missing.safetensors"}
+                ],
+            }
+            config_path.write_text(json.dumps(config))
+
+            def fail_hub_download(*args, **kwargs):
+                pytest.fail("local missing processor state should not call hf_hub_download")
+
+            monkeypatch.setattr("lerobot.processor.pipeline.hf_hub_download", fail_hub_download)
+
+            with pytest.raises(FileNotFoundError, match="missing.safetensors.*local processor pipeline"):
+                DataProcessorPipeline.from_pretrained(config_path, config_filename="ignored.json")
+    finally:
+        ProcessorStepRegistry.unregister("local_file_missing_state_step")
+
+
+def test_from_pretrained_hub_source_missing_local_state_still_calls_hub(monkeypatch, tmp_path):
+    """Hub sources still fall back to hf_hub_download for state files."""
+
+    @ProcessorStepRegistry.register("hub_state_step")
+    class HubStateStep(ProcessorStep):
+        def __init__(self):
+            self.value = torch.tensor(0)
+
+        def __call__(self, transition: EnvTransition) -> EnvTransition:
+            return transition
+
+        def transform_features(
+            self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+        ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+            return features
+
+        def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+            self.value = state["value"]
+
+    try:
+        state_path = tmp_path / "downloaded.safetensors"
+        save_file({"value": torch.tensor(7)}, state_path)
+        loaded_config = {
+            "name": "HubStatePipeline",
+            "steps": [{"registry_name": "hub_state_step", "state_file": "hub_state.safetensors"}],
+        }
+        calls = []
+
+        def fake_load_config(cls, model_id, config_filename, hub_download_kwargs):
+            return loaded_config, tmp_path / "hub_cache"
+
+        def fake_hub_download(**kwargs):
+            calls.append(kwargs)
+            return str(state_path)
+
+        monkeypatch.setattr(DataProcessorPipeline, "_load_config", classmethod(fake_load_config))
+        monkeypatch.setattr("lerobot.processor.pipeline.hf_hub_download", fake_hub_download)
+
+        pipeline = DataProcessorPipeline.from_pretrained("user/repo", config_filename="processor.json")
+
+        assert calls == [
+            {
+                "repo_id": "user/repo",
+                "filename": "hub_state.safetensors",
+                "repo_type": "model",
+                "force_download": False,
+                "resume_download": None,
+                "proxies": None,
+                "token": None,
+                "cache_dir": None,
+                "local_files_only": False,
+                "revision": None,
+            }
+        ]
+        assert pipeline.steps[0].value.item() == 7
+    finally:
+        ProcessorStepRegistry.unregister("hub_state_step")
+
+
 # Config Validation Tests
 
 

tests/test_rollout.py

@@ -17,6 +17,8 @@
 from __future__ import annotations
 
 import dataclasses
+import sys
+from types import SimpleNamespace
 from unittest.mock import MagicMock
 
 import pytest
@@ -106,6 +108,116 @@ def test_sentry_config_defaults():
     assert cfg.target_video_file_size_mb is None
 
 
+def test_rollout_config_passes_policy_pretrained_revision(monkeypatch):
+    from lerobot.configs import PreTrainedConfig, parser
+    from lerobot.rollout import RolloutConfig
+    from tests.mocks.mock_robot import MockRobotConfig
+
+    captured = {}
+
+    def fake_from_pretrained(cls, pretrained_name_or_path, **kwargs):
+        captured["pretrained_name_or_path"] = pretrained_name_or_path
+        captured.update(kwargs)
+        return SimpleNamespace(device="cpu", pretrained_revision=kwargs["revision"])
+
+    monkeypatch.setattr(parser, "get_yaml_overrides", lambda _: ["--pretrained_revision=yaml-sha"])
+    monkeypatch.setattr(
+        sys,
+        "argv",
+        ["lerobot-rollout", "--policy.path=user/policy", "--policy.pretrained_revision=cli-sha"],
+    )
+    monkeypatch.setattr(PreTrainedConfig, "from_pretrained", classmethod(fake_from_pretrained))
+
+    cfg = RolloutConfig(robot=MockRobotConfig())
+
+    assert captured["pretrained_name_or_path"] == "user/policy"
+    assert captured["revision"] == "cli-sha"
+    assert captured["cli_overrides"] == [
+        "--pretrained_revision=yaml-sha",
+        "--pretrained_revision=cli-sha",
+    ]
+    assert cfg.policy.pretrained_path == "user/policy"
+    assert cfg.policy.pretrained_revision == "cli-sha"
+
+
+def test_load_pretrained_policy_passes_revision(monkeypatch):
+    import lerobot.rollout.context as rollout_context
+
+    policy_config = SimpleNamespace(
+        type="mock",
+        use_peft=False,
+        pretrained_path="user/policy",
+        pretrained_revision="policy-sha",
+    )
+    policy_class = MagicMock()
+    loaded_policy = MagicMock()
+    policy_class.from_pretrained.return_value = loaded_policy
+    monkeypatch.setattr(rollout_context, "get_policy_class", lambda _: policy_class)
+
+    policy = rollout_context._load_pretrained_policy(policy_config)
+
+    assert policy is loaded_policy
+    policy_class.from_pretrained.assert_called_once_with(
+        "user/policy",
+        config=policy_config,
+        revision="policy-sha",
+    )
+
+
+def test_load_pretrained_peft_policy_keeps_adapter_and_base_revisions_separate(monkeypatch):
+    import lerobot.rollout.context as rollout_context
+
+    policy_config = SimpleNamespace(
+        type="mock",
+        use_peft=True,
+        pretrained_path="user/adapter",
+        pretrained_revision="adapter-sha",
+    )
+    policy_class = MagicMock()
+    base_policy = MagicMock()
+    policy_class.from_pretrained.return_value = base_policy
+    monkeypatch.setattr(rollout_context, "get_policy_class", lambda _: policy_class)
+
+    peft_config = SimpleNamespace(
+        base_model_name_or_path="user/base-policy",
+        revision="base-sha",
+    )
+    peft_config_from_pretrained = MagicMock(return_value=peft_config)
+    adapted_policy = MagicMock()
+    peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
+    require_package = MagicMock()
+    monkeypatch.setattr(rollout_context, "require_package", require_package)
+    monkeypatch.setattr(
+        rollout_context,
+        "PeftConfig",
+        SimpleNamespace(from_pretrained=peft_config_from_pretrained),
+        raising=False,
+    )
+    monkeypatch.setattr(
+        rollout_context,
+        "PeftModel",
+        SimpleNamespace(from_pretrained=peft_model_from_pretrained),
+        raising=False,
+    )
+
+    policy = rollout_context._load_pretrained_policy(policy_config)
+
+    assert policy is adapted_policy
+    require_package.assert_called_once_with("peft", extra="peft")
+    peft_config_from_pretrained.assert_called_once_with("user/adapter", revision="adapter-sha")
+    policy_class.from_pretrained.assert_called_once_with(
+        pretrained_name_or_path="user/base-policy",
+        config=policy_config,
+        revision="base-sha",
+    )
+    peft_model_from_pretrained.assert_called_once_with(
+        base_policy,
+        "user/adapter",
+        config=peft_config,
+        revision="adapter-sha",
+    )
+
+
 # ---------------------------------------------------------------------------
 # RolloutRingBuffer
 # ---------------------------------------------------------------------------