feat(output unit): adding support for output unit specification at dataset reading/training time

Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>

src/lerobot/configs/default.py

@@ -35,12 +35,17 @@ class DatasetConfig:
     revision: str | None = None
     use_imagenet_stats: bool = True
     video_backend: str = field(default_factory=get_safe_default_video_backend)
-    # When True, video frames are returned as uint8 tensors (0-255) instead of float32 (0.0-1.0).
+    # When True, RGB video frames are returned as uint8 tensors (0-255) instead of float32 (0.0-1.0).
     # This reduces memory and speeds up DataLoader IPC. The training pipeline handles the conversion.
     return_uint8: bool = False
+    # Physical unit depth maps are dequantized to at load time: "mm" (millimetres) or "m" (metres).
+    # Has no effect on datasets without depth cameras.
+    depth_output_unit: str = "mm"
     streaming: bool = False
 
     def __post_init__(self) -> None:
+        if self.depth_output_unit not in ("m", "mm"):
+            raise ValueError(f"depth_output_unit must be 'm' or 'mm', got {self.depth_output_unit!r}")
         if self.episodes is not None:
             if any(ep < 0 for ep in self.episodes):
                 raise ValueError(

src/lerobot/datasets/dataset_reader.py

@@ -54,6 +54,7 @@ class DatasetReader:
         delta_timestamps: dict[str, list[float]] | None,
         image_transforms: Callable | None,
         return_uint8: bool = False,
+        depth_output_unit: str = "mm",
     ):
         """Initialize the reader with metadata, filtering, and transform config.
 
@@ -71,6 +72,10 @@ class DatasetReader:
                 relative timestamp offsets for temporal context windows.
             image_transforms: Optional torchvision v2 transform applied to
                 visual features.
+            return_uint8: If True, return RGB video frames as raw uint8 tensors
+                instead of normalized float32.
+            depth_output_unit: Physical unit depth maps are dequantized to
+                (``"m"`` or ``"mm"``). Defaults to ``"mm"``.
         """
         self._meta = meta
         self.root = root
@@ -79,6 +84,7 @@ class DatasetReader:
         self._video_backend = video_backend
         self._image_transforms = image_transforms
         self._return_uint8 = return_uint8
+        self._depth_output_unit = depth_output_unit
 
         self.hf_dataset: datasets.Dataset | None = None
         self._absolute_to_relative_idx: dict[int, int] | None = None
@@ -266,6 +272,7 @@ class DatasetReader:
                     depth_max=depth_encoder.depth_max,
                     shift=depth_encoder.shift,
                     use_log=depth_encoder.use_log,
+                    output_unit=self._depth_output_unit,
                 )
             return vid_key, frames.squeeze(0)
 

src/lerobot/datasets/factory.py

@@ -96,6 +96,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                 revision=cfg.dataset.revision,
                 video_backend=cfg.dataset.video_backend,
                 return_uint8=True,
+                depth_output_unit=cfg.dataset.depth_output_unit,
                 tolerance_s=cfg.tolerance_s,
             )
         else:

src/lerobot/datasets/lerobot_dataset.py

@@ -58,6 +58,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         download_videos: bool = True,
         video_backend: str | None = None,
         return_uint8: bool = False,
+        depth_output_unit: str = "mm",
         batch_encoding_size: int = 1,
         camera_encoder: VideoEncoderConfig | None = None,
         depth_encoder: DepthEncoderConfig | None = None,
@@ -212,6 +213,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.revision = revision if revision else CODEBASE_VERSION
         self._video_backend = video_backend if video_backend else get_safe_default_video_backend()
         self._return_uint8 = return_uint8
+        self._depth_output_unit = depth_output_unit
         self._batch_encoding_size = batch_encoding_size
         self._encoder_threads = encoder_threads
 
@@ -252,6 +254,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             delta_timestamps=delta_timestamps,
             image_transforms=image_transforms,
             return_uint8=self._return_uint8,
+            depth_output_unit=self._depth_output_unit,
         )
 
         # Load actual data
@@ -321,6 +324,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 delta_timestamps=self.delta_timestamps,
                 image_transforms=self.image_transforms,
                 return_uint8=self._return_uint8,
+                depth_output_unit=self._depth_output_unit,
             )
         return self.reader
 
@@ -722,6 +726,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.episodes = None
         obj._video_backend = video_backend if video_backend is not None else get_safe_default_video_backend()
         obj._return_uint8 = False
+        obj._depth_output_unit = "mm"
         obj._batch_encoding_size = batch_encoding_size
         obj._encoder_threads = encoder_threads
 
@@ -820,6 +825,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.episodes = None
         obj._video_backend = video_backend if video_backend else get_safe_default_video_backend()
         obj._return_uint8 = False
+        obj._depth_output_unit = "mm"
         obj._batch_encoding_size = batch_encoding_size
 
         if obj._requested_root is not None: