chore(deps): bump the uv group across 1 directory with 2 updates

Bumps the uv group with 2 updates in the / directory: [setuptools](https://github.com/pypa/setuptools) and [pytest](https://github.com/pytest-dev/pytest).


Updates `setuptools` from 80.10.2 to 83.0.0
- [Release notes](https://github.com/pypa/setuptools/releases)
- [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst)
- [Commits](https://github.com/pypa/setuptools/compare/v80.10.2...v83.0.0)

Updates `pytest` from 8.4.2 to 9.0.3
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.4.2...9.0.3)

---
updated-dependencies:
- dependency-name: setuptools
  dependency-version: 83.0.0
  dependency-type: direct:development
  dependency-group: uv
- dependency-name: pytest
  dependency-version: 9.0.3
  dependency-type: direct:production
  dependency-group: uv
...

Signed-off-by: dependabot[bot] <support@github.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

@@ -14,7 +14,7 @@
 
 [build-system]
 requires = ["setuptools"]
-build-backend = "setuptools.build_meta"
+build-backend = "setuptools*"
 
 [project.urls]
 homepage = "https://huggingface.co/lerobot"
@@ -87,7 +87,7 @@ dependencies = [
 
     # Build tools (required by opencv-python-headless on some platforms)
     "cmake>=3.29.0.1,<4.2.0",
-    "setuptools>=71.0.0,<81.0.0",
+    "setuptools>=71.0.0,<84.0.0",
 ]
 
 # Optional dependencies
@@ -261,7 +261,7 @@ annotations = [
 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools>=1.73.1,<2.0.0", "mypy>=1.19.1", "ruff>=0.14.1", "lerobot[notebook]"]
 notebook = ["jupyter>=1.0.0,<2.0.0", "ipykernel>=6.0.0,<7.0.0"]
-test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
+test = ["pytest>=8.1.0,<10.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation
@@ -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/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/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
@@ -334,12 +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"]:
@@ -350,9 +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/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/rollout/context.py

@@ -24,6 +24,7 @@ from __future__ import annotations
 import logging
 from dataclasses import dataclass, field
 from threading import Event
+from typing import TYPE_CHECKING
 
 import torch
 
@@ -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__)
 
 
@@ -171,7 +179,7 @@ def _load_pretrained_policy(policy_config: PreTrainedConfig) -> PreTrainedPolicy
             revision=pretrained_revision,
         )
 
-    from peft import PeftConfig, PeftModel
+    require_package("peft", extra="peft")
 
     peft_path = policy_config.pretrained_path
     peft_config = PeftConfig.from_pretrained(peft_path, revision=pretrained_revision)
@@ -294,12 +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.

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
@@ -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_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

@@ -57,7 +57,7 @@ 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 (
@@ -68,6 +68,11 @@ 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
 
 
@@ -207,8 +212,6 @@ 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
@@ -312,7 +315,7 @@ 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.")

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

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_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 +1,79 @@
+# 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.
+
+import sys
+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)
+    monkeypatch.setitem(
+        sys.modules,
+        "peft",
+        SimpleNamespace(
+            PeftConfig=SimpleNamespace(from_pretrained=peft_config_from_pretrained),
+            PeftModel=SimpleNamespace(from_pretrained=peft_model_from_pretrained),
+        ),
+    )
+
+    policy = policy_factory.make_policy(cfg, ds_meta=dataset_meta)
+
+    assert policy is adapted_policy
+    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/test_rollout.py

@@ -185,18 +185,25 @@ def test_load_pretrained_peft_policy_keeps_adapter_and_base_revisions_separate(m
     peft_config_from_pretrained = MagicMock(return_value=peft_config)
     adapted_policy = MagicMock()
     peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
-    monkeypatch.setitem(
-        sys.modules,
-        "peft",
-        SimpleNamespace(
-            PeftConfig=SimpleNamespace(from_pretrained=peft_config_from_pretrained),
-            PeftModel=SimpleNamespace(from_pretrained=peft_model_from_pretrained),
-        ),
+    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",

uv.lock

@@ -402,10 +402,10 @@ name = "bddl"
 version = "1.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupytext" },
-    { name = "networkx" },
-    { name = "numpy" },
-    { name = "pytest" },
+    { name = "jupytext", marker = "sys_platform == 'linux'" },
+    { name = "networkx", marker = "sys_platform == 'linux'" },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "pytest", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5c/37/0211f82891a9f14efcfd2b2096f8d9e4351398ad637fdd1ee59cfc580b0e/bddl-1.0.1.tar.gz", hash = "sha256:1fa4e6e5050b93888ff6fd8455c39bfb29d3864ce06b4c37c0f781f513a2ae26", size = 164809, upload-time = "2022-03-08T01:48:23.564Z" }
 
@@ -1010,7 +1010,7 @@ name = "cuda-bindings"
 version = "12.9.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder" },
+    { name = "cuda-pathfinder", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/32/45/557d4ed1fa54f0c7db8aee083229f624990d69f7d00f55477eed5c7e169a/cuda_bindings-12.9.7-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0666d3c082ef8f4b2d670950589373550e9f3bf564d635dd883f24a0b40402ff", size = 7071026, upload-time = "2026-05-27T18:44:13.356Z" },
@@ -1043,37 +1043,37 @@ wheels = [
 
 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime-cu12" },
+    { name = "nvidia-cuda-runtime-cu12", marker = "sys_platform == 'linux'" },
 ]
 cufft = [
-    { name = "nvidia-cufft-cu12" },
+    { name = "nvidia-cufft-cu12", marker = "sys_platform == 'linux'" },
 ]
 cufile = [
-    { name = "nvidia-cufile-cu12" },
+    { name = "nvidia-cufile-cu12", marker = "sys_platform == 'linux'" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti-cu12" },
+    { name = "nvidia-cuda-cupti-cu12", marker = "sys_platform == 'linux'" },
 ]
 curand = [
-    { name = "nvidia-curand-cu12" },
+    { name = "nvidia-curand-cu12", marker = "sys_platform == 'linux'" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver-cu12" },
+    { name = "nvidia-cusolver-cu12", marker = "sys_platform == 'linux'" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse-cu12" },
+    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc-cu12" },
+    { name = "nvidia-cuda-nvrtc-cu12", marker = "sys_platform == 'linux'" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx-cu12" },
+    { name = "nvidia-nvtx-cu12", marker = "sys_platform == 'linux'" },
 ]
 
 [[package]]
@@ -1145,7 +1145,7 @@ name = "decord"
 version = "0.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy" },
+    { name = "numpy", marker = "(platform_machine != 'arm64' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/11/79/936af42edf90a7bd4e41a6cac89c913d4b47fa48a26b042d5129a9242ee3/decord-0.6.0-py3-none-manylinux2010_x86_64.whl", hash = "sha256:51997f20be8958e23b7c4061ba45d0efcd86bffd5fe81c695d0befee0d442976", size = 13602299, upload-time = "2021-06-14T21:30:55.486Z" },
@@ -1283,10 +1283,10 @@ resolution-markers = [
     "python_full_version == '3.14.*' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py" },
-    { name = "attrs" },
-    { name = "numpy" },
-    { name = "wrapt" },
+    { name = "absl-py", marker = "python_full_version >= '3.14'" },
+    { name = "attrs", marker = "python_full_version >= '3.14'" },
+    { name = "numpy", marker = "python_full_version >= '3.14'" },
+    { name = "wrapt", marker = "python_full_version >= '3.14'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a6/83/ce29720ccf934c6cfa9b9c95ebbe96558386e66886626066632b5e44afed/dm_tree-0.1.9.tar.gz", hash = "sha256:a4c7db3d3935a5a2d5e4b383fc26c6b0cd6f78c6d4605d3e7b518800ecd5342b", size = 35623, upload-time = "2025-01-30T20:45:37.13Z" }
 wheels = [
@@ -1324,10 +1324,10 @@ resolution-markers = [
     "python_full_version < '3.13' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py" },
-    { name = "attrs" },
-    { name = "numpy" },
-    { name = "wrapt" },
+    { name = "absl-py", marker = "python_full_version < '3.14'" },
+    { name = "attrs", marker = "python_full_version < '3.14'" },
+    { name = "numpy", marker = "python_full_version < '3.14'" },
+    { name = "wrapt", marker = "python_full_version < '3.14'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5a/66/a3ec619d22b6baffa5ab853e8dc6ec9d0c837127948af59bb15b988d7312/dm_tree-0.1.10.tar.gz", hash = "sha256:22f37b599e01cc3402a17f79c257a802aebd8d326de05b54657650845956208a", size = 35748, upload-time = "2026-03-31T17:35:39.03Z" }
 wheels = [
@@ -1912,7 +1912,7 @@ name = "h5py"
 version = "3.16.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy" },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/db/33/acd0ce6863b6c0d7735007df01815403f5589a21ff8c2e1ee2587a38f548/h5py-3.16.0.tar.gz", hash = "sha256:a0dbaad796840ccaa67a4c144a0d0c8080073c34c76d5a6941d6818678ef2738", size = 446526, upload-time = "2026-03-06T13:49:08.07Z" }
 wheels = [
@@ -1956,23 +1956,23 @@ name = "hf-libero"
 version = "0.1.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "bddl" },
-    { name = "cloudpickle" },
-    { name = "easydict" },
-    { name = "einops" },
-    { name = "future" },
-    { name = "gymnasium" },
-    { name = "hf-egl-probe" },
-    { name = "hydra-core" },
-    { name = "matplotlib" },
-    { name = "mujoco" },
-    { name = "numpy" },
-    { name = "opencv-python" },
-    { name = "robomimic" },
-    { name = "robosuite" },
-    { name = "thop" },
-    { name = "transformers" },
-    { name = "wandb" },
+    { name = "bddl", marker = "sys_platform == 'linux'" },
+    { name = "cloudpickle", marker = "sys_platform == 'linux'" },
+    { name = "easydict", marker = "sys_platform == 'linux'" },
+    { name = "einops", marker = "sys_platform == 'linux'" },
+    { name = "future", marker = "sys_platform == 'linux'" },
+    { name = "gymnasium", marker = "sys_platform == 'linux'" },
+    { name = "hf-egl-probe", marker = "sys_platform == 'linux'" },
+    { name = "hydra-core", marker = "sys_platform == 'linux'" },
+    { name = "matplotlib", marker = "sys_platform == 'linux'" },
+    { name = "mujoco", marker = "sys_platform == 'linux'" },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "opencv-python", marker = "sys_platform == 'linux'" },
+    { name = "robomimic", marker = "sys_platform == 'linux'" },
+    { name = "robosuite", marker = "sys_platform == 'linux'" },
+    { name = "thop", marker = "sys_platform == 'linux'" },
+    { name = "transformers", marker = "sys_platform == 'linux'" },
+    { name = "wandb", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/af/aa/4e9eb8715e0bff9cb6553db563a35d253393097d446f82bd53575e8b253d/hf_libero-0.1.4.tar.gz", hash = "sha256:c058d67ad5a2b589529c14d614282ef4cca3a7763dafa134f58a6c9039657e34", size = 2961319, upload-time = "2026-06-10T09:56:13.994Z" }
 wheels = [
@@ -2123,9 +2123,9 @@ name = "hydra-core"
 version = "1.3.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "antlr4-python3-runtime" },
-    { name = "omegaconf" },
-    { name = "packaging" },
+    { name = "antlr4-python3-runtime", marker = "sys_platform == 'linux'" },
+    { name = "omegaconf", marker = "sys_platform == 'linux'" },
+    { name = "packaging", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/10/dd/220f0e91743136725352497e98540772a01fc7c3ab96ff16c3c74424e984/hydra_core-1.3.4.tar.gz", hash = "sha256:ad0f7b05a0242255a8984d5a4ed2f6847f7b783ed727368a2c0155ec52d6c34c", size = 3263348, upload-time = "2026-07-04T16:25:38.891Z" }
 wheels = [
@@ -2678,11 +2678,11 @@ name = "jupytext"
 version = "1.19.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markdown-it-py" },
-    { name = "mdit-py-plugins" },
-    { name = "nbformat" },
-    { name = "packaging" },
-    { name = "pyyaml" },
+    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
+    { name = "mdit-py-plugins", marker = "sys_platform == 'linux'" },
+    { name = "nbformat", marker = "sys_platform == 'linux'" },
+    { name = "packaging", marker = "sys_platform == 'linux'" },
+    { name = "pyyaml", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a1/ca/473f8ebb101553fb2ea6ab1d34324d6677844c968947ac050c759d539f2c/jupytext-1.19.5.tar.gz", hash = "sha256:605026446d605aa54fd7f7fc69df6ae51c7a46053d4cebf05afdc64d66de3df0", size = 4600916, upload-time = "2026-07-21T22:00:29.198Z" }
 wheels = [
@@ -3472,7 +3472,7 @@ requires-dist = [
     { name = "pyrealsense2", marker = "sys_platform != 'darwin' and extra == 'intelrealsense'", specifier = ">=2.55.1.6486,<2.57.0" },
     { name = "pyrealsense2-macosx", marker = "sys_platform == 'darwin' and extra == 'intelrealsense'", specifier = ">=2.54,<2.57.0" },
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-    { name = "nvidia-cusparselt-cu12" },
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-    { name = "nvidia-nvshmem-cu12" },
-    { name = "setuptools" },
-    { name = "sympy" },
-    { name = "triton" },
-    { name = "typing-extensions" },
+    { name = "cuda-bindings", marker = "sys_platform == 'linux'" },
+    { name = "cuda-toolkit", extra = ["cublas", "cudart", "cufft", "cufile", "cupti", "curand", "cusolver", "cusparse", "nvjitlink", "nvrtc", "nvtx"], marker = "sys_platform == 'linux'" },
+    { name = "filelock", marker = "sys_platform == 'linux'" },
+    { name = "fsspec", marker = "sys_platform == 'linux'" },
+    { name = "jinja2", marker = "sys_platform == 'linux'" },
+    { name = "networkx", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cudnn-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparselt-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nccl-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvshmem-cu12", marker = "sys_platform == 'linux'" },
+    { name = "setuptools", marker = "sys_platform == 'linux'" },
+    { name = "sympy", marker = "sys_platform == 'linux'" },
+    { name = "triton", marker = "sys_platform == 'linux'" },
+    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
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-    { name = "numpy" },
-    { name = "pillow" },
-    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" } },
+    { name = "numpy", marker = "sys_platform != 'linux'" },
+    { name = "pillow", marker = "sys_platform != 'linux'" },
+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux'" },
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@@ -6730,9 +6730,9 @@ resolution-markers = [
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-    { name = "pillow" },
-    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" } },
+    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "pillow", marker = "sys_platform == 'linux'" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
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@@ -7222,7 +7222,7 @@ name = "werkzeug"
 version = "3.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markupsafe" },
+    { name = "markupsafe", marker = "sys_platform == 'linux'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/dd/b2/381be8cfdee792dd117872481b6e378f85c957dd7c5bca38897b08f765fd/werkzeug-3.1.8.tar.gz", hash = "sha256:9bad61a4268dac112f1c5cd4630a56ede601b6ed420300677a869083d70a4c44", size = 875852, upload-time = "2026-04-02T18:49:14.268Z" }
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