test(visualization): fixing visualization tests

numeric values before calling `is_integer()`

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

docs/source/cameras.mdx

@@ -157,6 +157,14 @@ finally:
 </hfoption>
 </hfoptions>
 
+### Working with depth
+
+The Intel RealSense and Reachy 2 cameras can capture both color and depth in lockstep. Calling `read()` returns the **color** frame as `(H, W, 3)` `uint8`. Calling `read_depth()` returns the **depth map** as `(H, W, 1)` `uint16`, where each pixel value is the distance from the sensor expressed in **millimetres**. A pixel value of `0` typically means "no measurement available" (out-of-range, occluded, or low-confidence).
+
+During recording, the control loop peeks the freshest buffered frames non-blockingly via `read_latest()` (color) and `read_latest_depth()` (depth), adding the depth map as a sibling feature (e.g. `front_depth` next to `front`).
+
+For how depth streams are stored and encoded when recording a dataset, see the [Depth streams](./video_encoding_parameters#depth-streams) section of the video encoding guide.
+
 ## Use your phone's camera
 
 <hfoptions id="use phone">

docs/source/hil_data_collection.mdx

@@ -57,11 +57,11 @@ The `lerobot-rollout --strategy.type=dagger` mode requires **teleoperators with
 
 **Compatible teleoperators:**
 
-- `openarm_mini` - OpenArm Mini
+- `bi_openarm_mini` - Bimanual OpenArm Mini
 - `so_leader` - SO100 / SO101 leader arm
 
 > [!IMPORTANT]
-> The provided commands default to `bi_openarm_follower` + `openarm_mini`.
+> The provided commands default to `bi_openarm_follower` + `bi_openarm_mini`.
 > `so_follower` + `so_leader` configs are also registered and can be used via CLI flags.
 
 ---
@@ -104,9 +104,9 @@ lerobot-rollout --strategy.type=dagger \
     --robot.right_arm_config.port=can0 \
     --robot.right_arm_config.side=right \
     --robot.cameras='{left_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}, right_wrist: {type: opencv, index_or_path: "/dev/video4", width: 1280, height: 720, fps: 30}, base: {type: opencv, index_or_path: "/dev/video2", width: 640, height: 480, fps: 30}}' \
-    --teleop.type=openarm_mini \
+    --teleop.type=bi_openarm_mini \
-    --teleop.port_left=/dev/ttyACM0 \
+    --teleop.left_arm_config.port=/dev/ttyACM0 \
-    --teleop.port_right=/dev/ttyACM1 \
+    --teleop.right_arm_config.port=/dev/ttyACM1 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
     --dataset.repo_id=your-username/rollout_hil_dataset \
     --dataset.single_task="Fold the T-shirt properly" \
@@ -131,9 +131,9 @@ lerobot-rollout --strategy.type=dagger \
     --robot.right_arm_config.port=can0 \
     --robot.right_arm_config.side=right \
     --robot.cameras='{left_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}, right_wrist: {type: opencv, index_or_path: "/dev/video4", width: 1280, height: 720, fps: 30}, base: {type: opencv, index_or_path: "/dev/video2", width: 640, height: 480, fps: 30}}' \
-    --teleop.type=openarm_mini \
+    --teleop.type=bi_openarm_mini \
-    --teleop.port_left=/dev/ttyACM0 \
+    --teleop.left_arm_config.port=/dev/ttyACM0 \
-    --teleop.port_right=/dev/ttyACM1 \
+    --teleop.right_arm_config.port=/dev/ttyACM1 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
     --dataset.repo_id=your-username/rollout_hil_rtc_dataset \
     --dataset.single_task="Fold the T-shirt properly" \

docs/source/inference.mdx

@@ -117,7 +117,7 @@ lerobot-rollout \
     --strategy.num_episodes=20 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
     --robot.type=bi_openarm_follower \
-    --teleop.type=openarm_mini \
+    --teleop.type=bi_openarm_mini \
     --dataset.repo_id=${HF_USER}/rollout_hil_data \
     --dataset.single_task="Fold the T-shirt"
 ```

docs/source/using_dataset_tools.mdx

@@ -11,8 +11,9 @@ LeRobot provides several utilities for manipulating datasets:
 3. **Merge Datasets** - Combine multiple datasets into one. The datasets must have identical features, and episodes are concatenated in the order specified in `repo_ids`
 4. **Add Features** - Add new features to a dataset
 5. **Remove Features** - Remove features from a dataset
-6. **Convert to Video** - Convert image-based datasets to video format for efficient storage
+6. **Convert to Video** - Convert image-based datasets to video format for efficient storage (RGB and depth cameras are encoded with separate encoders)
-7. **Show the Info of Datasets** - Show the summary of datasets information such as number of episode etc.
+7. **Re-encode Videos** - Re-encode an existing video dataset's RGB and/or depth streams with new encoder settings
+8. **Show the Info of Datasets** - Show the summary of datasets information such as number of episode etc.
 
 The core implementation is in `lerobot.datasets.dataset_tools`.
 An example script detailing how to use the tools API is available in `examples/dataset/use_dataset_tools.py`.
@@ -122,6 +123,15 @@ lerobot-edit-dataset \
     --operation.camera_encoder.g 2 \
     --operation.camera_encoder.crf 30
 
+# Convert a dataset that includes depth maps, customizing the depth encoder
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type convert_image_to_video \
+    --operation.output_dir outputs/pusht_video \
+    --operation.depth_encoder.depth_min 0.01 \
+    --operation.depth_encoder.depth_max 10.0 \
+    --operation.depth_encoder.use_log true
+
 # Convert only specific episodes
 lerobot-edit-dataset \
     --repo_id lerobot/pusht_image \
@@ -147,11 +157,42 @@ lerobot-edit-dataset \
 **Parameters:**
 
 - `output_dir`: Custom output directory (optional - by default uses `new_repo_id` or `{repo_id}_video`)
-- `camera_encoder`: Video encoder settings — all sub-fields accessible via `--operation.camera_encoder.<field>. See [Video Encoding Parameters](./video_encoding_parameters) for more details.
+- `camera_encoder`: Video encoder settings applied to RGB cameras — all sub-fields accessible via `--operation.camera_encoder.<field>`. See [Video Encoding Parameters](./video_encoding_parameters) for more details.
+- `depth_encoder`: Video encoder settings applied to depth-map cameras (e.g. from an Intel RealSense). In addition to the standard encoder fields it exposes the depth quantization knobs (`depth_min`, `depth_max`, `shift`, `use_log`), accessible via `--operation.depth_encoder.<field>`. These quantization settings are persisted to the dataset metadata so depth can be dequantized back to physical units on load. See the [Depth streams](./video_encoding_parameters#depth-streams) section for details.
 - `episode_indices`: List of specific episodes to convert (default: all episodes)
 - `num_workers`: Number of parallel workers for processing (default: 4)
 
-**Note:** The resulting dataset will be a proper LeRobotDataset with all cameras encoded as videos in the `videos/` directory, with parquet files containing only metadata (no raw image data). All episodes, stats, and tasks are preserved.
+**Note:** The resulting dataset will be a proper LeRobotDataset with all cameras encoded as videos in the `videos/` directory, with parquet files containing only metadata (no raw image data). Depth-map cameras are detected automatically and routed to the `depth_encoder`, while RGB cameras use the `camera_encoder`. All episodes, stats, and tasks are preserved.
+
+#### Re-encode Videos
+
+Re-encode the videos of an existing video dataset with different encoder settings, without going back to raw frames. RGB videos use the `camera_encoder` and depth videos use the `depth_encoder`. Provide only the encoder(s) you want to re-encode; the other stream type is left untouched.
+
+```bash
+# Re-encode all RGB videos with new settings (saves to lerobot/pusht_reencoded by default)
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht \
+    --operation.type reencode_videos \
+    --operation.camera_encoder.vcodec h264 \
+    --operation.camera_encoder.pix_fmt yuv420p \
+    --operation.camera_encoder.crf 23
+
+# Re-encode both RGB and depth videos in a dataset with depth maps
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_depth \
+    --operation.type reencode_videos \
+    --operation.camera_encoder.vcodec libx264 \
+    --operation.depth_encoder.vcodec ffv1
+```
+
+**Parameters:**
+
+- `camera_encoder`: Encoder settings applied to every RGB video. Omit to skip re-encoding RGB videos.
+- `depth_encoder`: Encoder settings applied to every depth video. Omit to skip re-encoding depth videos.
+- `num_workers`: Number of parallel workers for processing.
+
+> [!NOTE]
+> When re-encoding depth videos, the existing depth quantization parameters (`depth_min`, `depth_max`, `shift`, `use_log`) and the `is_depth_map` flag are **preserved** — re-encoding only changes the codec/quality of the stored stream, not how depth is dequantized on load.
 
 ### Show the information of datasets
 

docs/source/video_encoding_parameters.mdx

@@ -65,6 +65,76 @@ All flags below are prefixed with `--dataset.camera_encoder.` on the CLI.
 
 ---
 
+## Depth streams
+
+Depth maps (Intel RealSense, Reachy 2) are stored as their **own video streams** alongside the RGB streams. Raw depth (`uint16` millimetres or `float32` metres) can't survive an 8-bit codec, so LeRobot **quantizes** each map to a 12-bit code (`[0, 4095]`) — logarithmically by default, to match the `1/depth` error profile of depth sensors — then packs it into a high-bit-depth pixel format (`gray12le`) and encodes it with a 12-bit codec.
+
+```mermaid
+flowchart LR
+    A["Raw depth (uint16 mm / float32 m)"] --> B["Clip to depth_min, depth_max"]
+    B --> C["Quantize to 12-bit code 0–4095 (log or linear)"]
+    C --> D["Pack into gray12le"]
+    D --> E["Encode video (hevc Main 12)"]
+    E --> F[("MP4 + metadata: depth_min/max, shift, use_log")]
+    F -. "load time (depth_output_unit)" .-> G["Dequantize to mm or m"]
+
+    classDef input fill:#e3f2fd,stroke:#1565c0,color:#0d47a1;
+    classDef encode fill:#ede7f6,stroke:#5e35b1,color:#311b92;
+    classDef store fill:#fff8e1,stroke:#f9a825,color:#e65100;
+    classDef load fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20;
+
+    class A input;
+    class B,C,D,E encode;
+    class F store;
+    class G load;
+```
+
+Configure the depth pipeline through a parallel **`depth_encoder`** block (`DepthEncoderConfig`). It inherits every `VideoEncoderConfig` field (`vcodec`, `pix_fmt`, `crf`, …) and adds four quantizer knobs, set via `--dataset.depth_encoder.<field>`:
+
+```bash
+lerobot-record \
+    ... \
+    --dataset.depth_encoder.vcodec=hevc \
+    --dataset.depth_encoder.depth_min=0.05 \
+    --dataset.depth_encoder.depth_max=5.0 \
+    --dataset.depth_encoder.use_log=true
+```
+
+| Parameter   | Type    | Default      | Description                                                                                                                       |
+| ----------- | ------- | ------------ | --------------------------------------------------------------------------------------------------------------------------------- |
+| `vcodec`    | `str`   | `"hevc"`     | Defaults to HEVC Main 12 (a 12-bit-capable codec). `ffv1` is a lossless alternative.                                              |
+| `pix_fmt`   | `str`   | `"gray12le"` | Single-channel 12-bit pixel format used to carry the quantized codes.                                                             |
+| `depth_min` | `float` | `0.01`       | Depth in metres mapped to quantum `0`. Values below are clipped on decode.                                                        |
+| `depth_max` | `float` | `10.0`       | Depth in metres mapped to quantum `4095`. Values above are clipped on decode.                                                     |
+| `shift`     | `float` | `3.5`        | Pre-log offset (metres) used in logarithmic quantization for numerical stability near zero. Must satisfy `depth_min + shift > 0`. |
+| `use_log`   | `bool`  | `True`       | If `true`, quantize in log-space (recommended for typical depth sensors). Set to `false` for uniform/linear quantization.         |
+
+> [!TIP]
+> `depth_min`, `depth_max`, and `shift` are always interpreted in **metres**, regardless of the input depth's unit. Inputs are auto-detected: integer arrays (e.g. `uint16` millimetres straight from a RealSense) are treated as millimetres, floating arrays as metres.
+> Pick `depth_min` / `depth_max` to bracket the actual working range of your sensor — quanta outside that range saturate, which can crush detail at the boundaries.
+
+Depth features are flagged with `"is_depth_map": true` in `meta/info.json`, and their quantizer settings (`video.depth_min`, `video.depth_max`, `video.shift`, `video.use_log`) are persisted — which is what lets depth be **dequantized back to physical units** on load.
+
+### Output unit at load time
+
+`depth_encoder` is a **record-time** concern. The unit that depth maps are dequantized to on _load_ (e.g. during training) is set separately by the read-time flag `--dataset.depth_output_unit`:
+
+```bash
+lerobot-train \
+    --dataset.repo_id=<my_username>/<my_dataset_name> \
+    --dataset.depth_output_unit=m \
+    --policy.type=act
+```
+
+| Parameter           | Type  | Default | Description                                                                                  |
+| ------------------- | ----- | ------- | -------------------------------------------------------------------------------------------- |
+| `depth_output_unit` | `str` | `"mm"`  | Physical unit depth maps are dequantized to on load: `"mm"` (millimetres) or `"m"` (metres). |
+
+> [!TIP]
+> This is purely a decode-time presentation choice — it does **not** alter the stored video or its metadata, so the same dataset can be read as `mm` or `m` without re-encoding. It has no effect on datasets without depth cameras.
+
+---
+
 ## Persistence in dataset metadata
 
 After the first episode of a video stream is encoded, the encoder configuration is **persisted into the dataset metadata** (`meta/info.json`) under each video feature, alongside the values probed from the file itself. For a video feature `observation.images.<camera>`, the layout in `info.json` is:
@@ -82,7 +152,7 @@ After the first episode of a video stream is encoded, the encoder configuration
         "video.pix_fmt": "yuv420p",
         "video.fps": 30,
         "video.channels": 3,
-        "video.is_depth_map": false,
+        "is_depth_map": false,
         "video.g": 2,
         "video.crf": 30,
         "video.preset": "fast",
@@ -97,7 +167,7 @@ After the first episode of a video stream is encoded, the encoder configuration
 
 Two sources contribute to the `info` block:
 
-- **Stream-derived** (read back from the encoded MP4 with PyAV): `video.height`, `video.width`, `video.codec`, `video.pix_fmt`, `video.fps`, `video.channels`, `video.is_depth_map`, plus `audio.*` if an audio stream is present.
+- **Stream-derived** (read back from the encoded MP4 with PyAV): `video.height`, `video.width`, `video.codec`, `video.pix_fmt`, `video.fps`, `video.channels`, `is_depth_map`, plus `audio.*` if an audio stream is present.
 - **Encoder-derived** (taken from `VideoEncoderConfig`): `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.video_backend`, `video.extra_options`.
 
 <Tip>

src/lerobot/annotations/steerable_pipeline/frames.py

@@ -281,7 +281,7 @@ class VideoFrameProvider:
             reencode_video(
                 src,
                 out_path,
-                camera_encoder=encoder,
+                video_encoder=encoder,
                 overwrite=True,
                 start_time_s=from_timestamp,
                 end_time_s=to_timestamp,

src/lerobot/annotations/steerable_pipeline/writer.py

@@ -54,6 +54,7 @@ from typing import Any
 import pyarrow as pa
 import pyarrow.parquet as pq
 
+from lerobot.datasets.io_utils import write_table_one_row_group_per_episode
 from lerobot.datasets.language import (
     EVENT_ONLY_STYLES,
     LANGUAGE_EVENTS,
@@ -274,12 +275,11 @@ class LanguageColumnsWriter:
         new_table = self._materialize_table(
             table, per_row_persistent, per_row_events, drop_old=self.drop_existing_subtask_index
         )
-        # Atomic replace: write to a sibling tmp path and rename so a crash
+        # Re-emit one row group per episode (a bulk pq.write_table would collapse
-        # mid-write can't leave a half-written shard that ``pq.read_table``
+        # them into one). Write to a sibling tmp path and atomically rename so a
-        # would then fail to open. ``Path.replace`` is atomic on POSIX +
+        # crash mid-write can't leave a half-written shard.
-        # Windows when source and target sit on the same filesystem.
         tmp_path = path.with_suffix(path.suffix + ".tmp")
-        pq.write_table(new_table, tmp_path)
+        write_table_one_row_group_per_episode(new_table, tmp_path)
         tmp_path.replace(path)
 
     def _materialize_table(

src/lerobot/async_inference/helpers.py

@@ -105,8 +105,9 @@ def raw_observation_to_observation(
 
 
 def prepare_image(image: torch.Tensor) -> torch.Tensor:
-    """Minimal preprocessing to turn int8 images to float32 in [0, 1], and create a memory-contiguous tensor"""
+    """Minimal preprocessing to turn RGB uint8 images to float32 in [0, 1], and create a memory-contiguous tensor"""
-    image = image.type(torch.float32) / 255
+    if image.dtype == torch.uint8:
+        image = image.type(torch.float32) / 255
     image = image.contiguous()
 
     return image

src/lerobot/cameras/opencv/camera_opencv.py

@@ -436,7 +436,7 @@ class OpenCVCamera(Camera):
         Internal loop run by the background thread for asynchronous reading.
 
         On each iteration:
-        1. Reads a color frame
+        1. Reads a color frame (blocking call)
         2. Stores result in latest_frame and updates timestamp (thread-safe)
         3. Sets new_frame_event to notify listeners
 
@@ -445,8 +445,9 @@ class OpenCVCamera(Camera):
         if self.stop_event is None:
             raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
 
+        stop_event = self.stop_event
         failure_count = 0
-        while not self.stop_event.is_set():
+        while not stop_event.is_set():
             try:
                 raw_frame = self._read_from_hardware()
                 processed_frame = self._postprocess_image(raw_frame)
@@ -484,6 +485,8 @@ class OpenCVCamera(Camera):
 
         if self.thread is not None and self.thread.is_alive():
             self.thread.join(timeout=2.0)
+            if self.thread.is_alive():
+                logger.warning(f"{self} read thread did not terminate within timeout.")
 
         self.thread = None
         self.stop_event = None

src/lerobot/cameras/realsense/camera_realsense.py

@@ -268,13 +268,13 @@ class RealSenseCamera(Camera):
             )
 
         if len(found_devices) > 1:
-            serial_numbers = [dev["serial_number"] for dev in found_devices]
+            serial_numbers = [dev["id"] for dev in found_devices]
             raise ValueError(
                 f"Multiple RealSense cameras found with name '{name}'. "
                 f"Please use a unique serial number instead. Found SNs: {serial_numbers}"
             )
 
-        serial_number = str(found_devices[0]["serial_number"])
+        serial_number = str(found_devices[0]["id"])
         return serial_number
 
     def _configure_rs_pipeline_config(self, rs_config: Any) -> None:
@@ -332,8 +332,8 @@ class RealSenseCamera(Camera):
         from the camera hardware via the RealSense pipeline.
 
         Returns:
-            np.ndarray: The depth map as a NumPy array (height, width)
+            np.ndarray: The depth map as a NumPy array (height, width, 1)
-                  of type `np.uint16` (raw depth values in millimeters) and rotation.
+                  of type `np.uint16` (raw depth values in millimeters).
 
         Raises:
             DeviceNotConnectedError: If the camera is not connected.
@@ -465,8 +465,8 @@ class RealSenseCamera(Camera):
         Internal loop run by the background thread for asynchronous reading.
 
         On each iteration:
-        1. Reads a color frame with 500ms timeout
+        1. Reads a color/depth frame (blocking call with 10s timeout)
-        2. Stores result in latest_frame and updates timestamp (thread-safe)
+        2. Stores result in latest_color_frame/latest_depth_frame and updates timestamp (thread-safe)
         3. Sets new_frame_event to notify listeners
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
@@ -474,8 +474,9 @@ class RealSenseCamera(Camera):
         if self.stop_event is None:
             raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
 
+        stop_event = self.stop_event
         failure_count = 0
-        while not self.stop_event.is_set():
+        while not stop_event.is_set():
             try:
                 frame = self._read_from_hardware()
                 color_frame_raw = frame.get_color_frame()
@@ -486,6 +487,8 @@ class RealSenseCamera(Camera):
                     depth_frame_raw = frame.get_depth_frame()
                     depth_frame = np.asanyarray(depth_frame_raw.get_data())
                     processed_depth_frame = self._postprocess_image(depth_frame, depth_frame=True)
+                    if processed_depth_frame.ndim == 2:  # (H, W) -> (H, W, 1)
+                        processed_depth_frame = processed_depth_frame[..., np.newaxis]
 
                 capture_time = time.perf_counter()
 
@@ -522,6 +525,8 @@ class RealSenseCamera(Camera):
 
         if self.thread is not None and self.thread.is_alive():
             self.thread.join(timeout=2.0)
+            if self.thread.is_alive():  # pragma: no cover
+                logger.warning(f"{self} read thread did not terminate within timeout.")
 
         self.thread = None
         self.stop_event = None
@@ -532,7 +537,6 @@ class RealSenseCamera(Camera):
             self.latest_timestamp = None
             self.new_frame_event.clear()
 
-    # NOTE(Steven): Missing implementation for depth for now
     @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
@@ -575,7 +579,6 @@ class RealSenseCamera(Camera):
 
         return frame
 
-    # NOTE(Steven): Missing implementation for depth for now
     @check_if_not_connected
     def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
         """Return the most recent (color) frame captured immediately (Peeking).
@@ -611,6 +614,73 @@ class RealSenseCamera(Camera):
 
         return frame
 
+    @check_if_not_connected
+    def async_read_depth(self, timeout_ms: float = 200) -> NDArray[np.uint16]:
+        """Read the latest depth frame asynchronously, in millimeters.
+
+        Mirrors :meth:`async_read` but returns the depth stream rather than the
+        color stream. Output is ``np.uint16`` of shape ``(H, W, 1)``, where each
+        pixel is the distance from the sensor in millimeters.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If ``use_depth`` is ``False`` for this camera, or if
+                the background read thread is not running.
+            TimeoutError: If no frame becomes available within ``timeout_ms``.
+        """
+        if not self.use_depth:
+            raise RuntimeError(f"{self}: cannot read depth — camera was configured with use_depth=False.")
+
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
+
+        if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
+            raise TimeoutError(f"Timed out waiting for depth frame from camera {self} after {timeout_ms} ms.")
+
+        with self.frame_lock:
+            depth_frame = self.latest_depth_frame
+            self.new_frame_event.clear()
+
+        if depth_frame is None:
+            raise RuntimeError(f"Internal error: Event set but no depth frame available for {self}.")
+
+        return depth_frame
+
+    @check_if_not_connected
+    def read_latest_depth(self, max_age_ms: int = 500) -> NDArray[Any]:
+        """Return the most recent depth frame in millimeters (peeking).
+
+        Non-blocking counterpart of :meth:`read_latest` for the depth stream.
+        Output is ``np.uint16`` of shape ``(H, W, 1)``, where each pixel is the
+        distance from the sensor in millimeters.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If ``use_depth`` is ``False`` for this camera, or if
+                no depth frame has been captured yet.
+            TimeoutError: If the latest depth frame is older than ``max_age_ms``.
+        """
+        if not self.use_depth:
+            raise RuntimeError(f"{self}: cannot read depth — camera was configured with use_depth=False.")
+
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
+
+        with self.frame_lock:
+            depth_frame = self.latest_depth_frame
+            timestamp = self.latest_timestamp
+
+        if depth_frame is None or timestamp is None:
+            raise RuntimeError(f"{self} has not captured any depth frames yet.")
+
+        age_ms = (time.perf_counter() - timestamp) * 1e3
+        if age_ms > max_age_ms:
+            raise TimeoutError(
+                f"{self} latest depth frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
+            )
+
+        return depth_frame
+
     def disconnect(self) -> None:
         """
         Disconnects from the camera, stops the pipeline, and cleans up resources.

src/lerobot/cameras/zmq/camera_zmq.py

@@ -249,8 +249,9 @@ class ZMQCamera(Camera):
         if self.stop_event is None:
             raise RuntimeError(f"{self}: stop_event is not initialized.")
 
+        stop_event = self.stop_event
         failure_count = 0
-        while not self.stop_event.is_set():
+        while not stop_event.is_set():
             try:
                 frame = self._read_from_hardware()
                 capture_time = time.perf_counter()
@@ -292,6 +293,8 @@ class ZMQCamera(Camera):
 
         if self.thread is not None and self.thread.is_alive():
             self.thread.join(timeout=2.0)
+            if self.thread.is_alive():
+                logger.warning(f"{self} read thread did not terminate within timeout.")
 
         self.thread = None
         self.stop_event = None

src/lerobot/common/wandb_utils.py

@@ -180,26 +180,24 @@ class WandBLogger:
                 self._wandb_custom_step_key.add(new_custom_key)
                 self._wandb.define_metric(new_custom_key, hidden=True)
 
-        batch_data = {}
         for k, v in d.items():
-            # Skip the custom step key here, it's added to the batch below.
-            if custom_step_key is not None and k == custom_step_key:
-                continue
-
             if not isinstance(v, (int | float | str)):
                 logging.warning(
                     f'WandB logging of key "{k}" was ignored as its type "{type(v)}" is not handled by this wrapper.'
                 )
                 continue
 
-            batch_data[f"{mode}/{k}"] = v
+            # Do not log the custom step key itself.
+            if self._wandb_custom_step_key is not None and k in self._wandb_custom_step_key:
+                continue
 
-        if batch_data:
             if custom_step_key is not None:
-                batch_data[f"{mode}/{custom_step_key}"] = d[custom_step_key]
+                value_custom_step = d[custom_step_key]
-                self._wandb.log(batch_data)
+                data = {f"{mode}/{k}": v, f"{mode}/{custom_step_key}": value_custom_step}
-            else:
+                self._wandb.log(data)
-                self._wandb.log(data=batch_data, step=step)
+                continue
+
+            self._wandb.log(data={f"{mode}/{k}": v}, step=step)
 
     def log_video(self, video_path: str, step: int, mode: str = "train"):
         if mode not in {"train", "eval"}:

src/lerobot/configs/__init__.py

@@ -35,8 +35,11 @@ from .types import (
 from .video import (
     VALID_VIDEO_CODECS,
     VIDEO_ENCODER_INFO_KEYS,
+    DepthEncoderConfig,
     VideoEncoderConfig,
     camera_encoder_defaults,
+    depth_encoder_defaults,
+    encoder_config_from_video_info,
 )
 
 __all__ = [
@@ -57,8 +60,12 @@ __all__ = [
     "WandBConfig",
     "load_recipe",
     "VideoEncoderConfig",
+    "DepthEncoderConfig",
     # Defaults
     "camera_encoder_defaults",
+    "depth_encoder_defaults",
+    # Factories
+    "encoder_config_from_video_info",
     # Constants
     "VALID_VIDEO_CODECS",
     "VIDEO_ENCODER_INFO_KEYS",

src/lerobot/configs/dataset.py

@@ -18,7 +18,7 @@ from dataclasses import dataclass, field
 from datetime import datetime
 from pathlib import Path
 
-from .video import VideoEncoderConfig, camera_encoder_defaults
+from .video import DepthEncoderConfig, VideoEncoderConfig, camera_encoder_defaults, depth_encoder_defaults
 
 
 @dataclass
@@ -60,6 +60,8 @@ class DatasetRecordConfig:
     # Video encoder settings for camera MP4s (codec, quality, GOP, etc.). Tuned via CLI nested keys,
     # e.g. ``--dataset.camera_encoder.vcodec=h264`` (see ``VideoEncoderConfig``).
     camera_encoder: VideoEncoderConfig = field(default_factory=camera_encoder_defaults)
+    # Video encoder settings for depth-map MP4s (codec, quality, GOP, etc.). Tuned via CLI nested keys.
+    depth_encoder: DepthEncoderConfig = field(default_factory=depth_encoder_defaults)
     # Enable streaming video encoding: encode frames in real-time during capture instead
     # of writing PNG images first. Makes save_episode() near-instant. More info in the documentation: https://huggingface.co/docs/lerobot/streaming_video_encoding
     streaming_encoding: bool = False

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/configs/video.py

@@ -20,7 +20,7 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass, field
-from typing import Any
+from typing import Any, ClassVar, Self
 
 from lerobot.utils.import_utils import require_package
 
@@ -36,11 +36,12 @@ HW_VIDEO_CODECS = [
     "h264_vaapi",  # Linux Intel/AMD
     "h264_qsv",  # Intel Quick Sync
 ]
-VALID_VIDEO_CODECS: frozenset[str] = frozenset({"h264", "hevc", "libsvtav1", "auto", *HW_VIDEO_CODECS})
+VALID_VIDEO_CODECS: frozenset[str] = frozenset(
+    {"h264", "hevc", "libsvtav1", "ffv1", "auto", *HW_VIDEO_CODECS}
+)
 # Aliases for legacy video codec names.
 VIDEO_CODECS_ALIASES: dict[str, str] = {"av1": "libsvtav1"}
 
-
 LIBSVTAV1_DEFAULT_PRESET: int = 12
 
 # Keys persisted under ``features[*]["info"]`` as ``video.<name>`` (from :class:`VideoEncoderConfig`).
@@ -52,6 +53,19 @@ VIDEO_ENCODER_INFO_KEYS: frozenset[str] = frozenset(
     f"video.{name}" for name in VIDEO_ENCODER_INFO_FIELD_NAMES
 )
 
+# Default depth quantization and encoding parameters.
+DEPTH_QUANT_BITS: int = 12
+DEPTH_QMAX: int = (1 << DEPTH_QUANT_BITS) - 1  # 4095
+
+DEFAULT_DEPTH_MIN: float = 0.01
+DEFAULT_DEPTH_MAX: float = 10.0
+DEFAULT_DEPTH_SHIFT: float = 3.5
+DEFAULT_DEPTH_USE_LOG: bool = True
+DEFAULT_DEPTH_PIX_FMT: str = "gray12le"
+
+# Depth-specific tuning fields persisted under ``features[*]["info"]`` as ``video.<name>``.
+DEPTH_ENCODER_INFO_FIELD_NAMES: frozenset[str] = frozenset({"depth_min", "depth_max", "shift", "use_log"})
+
 
 @dataclass
 class VideoEncoderConfig:
@@ -86,6 +100,10 @@ class VideoEncoderConfig:
     video_backend: str = "pyav"
     extra_options: dict[str, Any] = field(default_factory=dict)
 
+    # Source-data channel count this encoder is expected to handle (3 for RGB,
+    # 1 for depth, etc.)
+    _DEFAULT_CHANNELS: ClassVar[int] = 3
+
     def __post_init__(self) -> None:
         self.resolve_vcodec()
         # Empty-constructor ergonomics: ``VideoEncoderConfig()`` must "just work".
@@ -94,9 +112,9 @@ class VideoEncoderConfig:
         self.validate()
 
     @classmethod
-    def from_video_info(cls, video_info: dict | None) -> VideoEncoderConfig:
+    def _kwargs_from_video_info(cls, video_info: dict | None) -> dict[str, Any]:
-        """Reconstruct a :class:`VideoEncoderConfig` from a video feature's ``info`` block.
+        """Parse the ``video.*`` keys of a feature ``info`` block into
-        Missing or ``None`` values fall back to the class defaults.
+        constructor kwargs.
         """
         video_info = video_info or {}
         kwargs: dict[str, Any] = {}
@@ -115,7 +133,15 @@ class VideoEncoderConfig:
                 continue
             kwargs[field_name] = value
 
-        return cls(**kwargs)
+        return kwargs
+
+    @classmethod
+    def from_video_info(cls, video_info: dict | None) -> Self:
+        """Reconstruct an encoder config from a video feature's ``info`` block.
+
+        Missing or ``None`` values fall back to the class defaults.
+        """
+        return cls(**cls._kwargs_from_video_info(video_info))
 
     def detect_available_encoders(self, encoders: list[str] | str) -> list[str]:
         """Return the subset of available encoders based on the specified video backend.
@@ -138,7 +164,9 @@ class VideoEncoderConfig:
             require_package("av", extra="dataset")
             from lerobot.datasets import check_video_encoder_parameters_pyav
 
-            check_video_encoder_parameters_pyav(self.vcodec, self.pix_fmt, self.get_codec_options())
+            check_video_encoder_parameters_pyav(
+                self.vcodec, self.pix_fmt, self.get_codec_options(), channels=self._DEFAULT_CHANNELS
+            )
 
     def resolve_vcodec(self) -> None:
         """Check ``vcodec`` and, when it is ``"auto"``, pick a concrete encoder.
@@ -218,6 +246,10 @@ class VideoEncoderConfig:
         elif self.vcodec == "h264_qsv":
             set_if("global_quality", self.crf)
             set_if("preset", self.preset)
+        elif self.vcodec == "ffv1":
+            # Lossless intra-frame codec. ``crf``/``preset``/``fast_decode``
+            # are not meaningful.
+            set_if("threads", encoder_threads)
         else:
             set_if("crf", self.crf)
             set_if("preset", self.preset)
@@ -233,3 +265,75 @@ class VideoEncoderConfig:
 def camera_encoder_defaults() -> VideoEncoderConfig:
     """Return a :class:`VideoEncoderConfig` with RGB-camera defaults."""
     return VideoEncoderConfig()
+
+
+@dataclass
+class DepthEncoderConfig(VideoEncoderConfig):
+    """Encoder configuration for depth-map streams.
+
+    Inherits the full :class:`VideoEncoderConfig` surface (codec, GOP, CRF,
+    preset, ``extra_options``…) and adds the four parameters of the depth
+    quantizer.
+
+    Defaults flip ``vcodec`` to ``"hevc"`` (Main 12 profile) and ``pix_fmt``
+    to ``"gray12le"``.
+
+
+    Attributes:
+        depth_min: Minimum depth in physical units (e.g. metres) represented
+            by quantum ``0``.
+        depth_max: Maximum depth represented by quantum :data:`DEPTH_QMAX`.
+        shift: Pre-log offset for numerical stability near zero.
+        use_log: ``True`` for logarithmic quantization (default; matches
+            sensor error profile), ``False`` for linear.
+    """
+
+    vcodec: str = "hevc"
+    pix_fmt: str = "gray12le"
+
+    depth_min: float = DEFAULT_DEPTH_MIN
+    depth_max: float = DEFAULT_DEPTH_MAX
+    shift: float = DEFAULT_DEPTH_SHIFT
+    use_log: bool = DEFAULT_DEPTH_USE_LOG
+
+    _DEFAULT_CHANNELS: ClassVar[int] = 1
+
+    @classmethod
+    def _kwargs_from_video_info(cls, video_info: dict | None) -> dict[str, Any]:
+        """Layer the depth-specific tuning (``depth_min`` / ``depth_max`` /
+        ``shift`` / ``use_log``) on top of the base parser. Missing keys
+        fall back to the class defaults.
+        """
+        kwargs = super()._kwargs_from_video_info(video_info)
+        video_info = video_info or {}
+        for name in DEPTH_ENCODER_INFO_FIELD_NAMES:
+            value = video_info.get(f"video.{name}")
+            if value is not None:
+                kwargs[name] = value
+        return kwargs
+
+
+def depth_encoder_defaults() -> DepthEncoderConfig:
+    """Return a :class:`DepthEncoderConfig` with depth-camera defaults."""
+    return DepthEncoderConfig()
+
+
+def encoder_config_from_video_info(video_info: dict | None) -> VideoEncoderConfig:
+    """Build the appropriate encoder config from a feature's ``info`` block.
+
+    Dispatches to :class:`DepthEncoderConfig` when the dict marks the feature
+    as a depth map and to :class:`VideoEncoderConfig`
+    otherwise.
+
+    Args:
+        video_info: A feature's ``info`` dict as persisted in ``info.json``,
+            or ``None`` (treated as an empty dict).
+
+    Returns:
+        A :class:`DepthEncoderConfig` for depth features, otherwise a
+        :class:`VideoEncoderConfig`.
+    """
+    video_info = video_info or {}
+    is_depth = bool(video_info.get("is_depth_map") or video_info.get("video.is_depth_map"))
+    cls: type[VideoEncoderConfig] = DepthEncoderConfig if is_depth else VideoEncoderConfig
+    return cls.from_video_info(video_info)

src/lerobot/datasets/aggregate.py

@@ -32,6 +32,7 @@ from .feature_utils import features_equal_for_merge, get_hf_features_from_featur
 from .io_utils import (
     get_file_size_in_mb,
     get_parquet_file_size_in_mb,
+    to_parquet_one_row_group_per_episode,
     to_parquet_with_hf_images,
     write_info,
     write_stats,
@@ -551,6 +552,7 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
             aggr_root=dst_meta.root,
             hf_features=hf_features,
             concatenate=concatenate_data,
+            one_row_group_per_episode=True,
         )
 
         # Record the mapping from source to actual destination
@@ -628,6 +630,7 @@ def append_or_create_parquet_file(
     aggr_root: Path = None,
     hf_features: datasets.Features | None = None,
     concatenate: bool = True,
+    one_row_group_per_episode: bool = False,
 ) -> tuple[dict[str, int], tuple[int, int]]:
     """Appends data to an existing parquet file or creates a new one based on size constraints.
 
@@ -645,6 +648,8 @@ def append_or_create_parquet_file(
         aggr_root: Root path for the aggregated dataset.
         hf_features: Optional HuggingFace Features schema for proper image typing.
         concatenate: When False, always rotate to a new file instead of appending to the current one.
+        one_row_group_per_episode: True for DATA parquet (emit one row group per episode); False for
+            the episodes-metadata parquet (already one row per episode).
 
     Returns:
         tuple: (updated_idx, (dst_chunk, dst_file)) where updated_idx is the index dict
@@ -657,6 +662,8 @@ def append_or_create_parquet_file(
         dst_path.parent.mkdir(parents=True, exist_ok=True)
         if contains_images:
             to_parquet_with_hf_images(df, dst_path, features=hf_features)
+        elif one_row_group_per_episode:
+            to_parquet_one_row_group_per_episode(df, dst_path)
         else:
             df.to_parquet(dst_path)
         return idx, (dst_chunk, dst_file)
@@ -683,6 +690,8 @@ def append_or_create_parquet_file(
 
     if contains_images:
         to_parquet_with_hf_images(final_df, target_path, features=hf_features)
+    elif one_row_group_per_episode:
+        to_parquet_one_row_group_per_episode(final_df, target_path)
     else:
         final_df.to_parquet(target_path)
 

src/lerobot/datasets/compute_stats.py

@@ -506,8 +506,10 @@ def compute_episode_stats(
         Each statistics dictionary contains min, max, mean, std, count, and quantiles.
 
     Note:
-        Image statistics are normalized to [0,1] range and have shape (3,1,1) for
+        For 'image'/'video' features, stats are computed per channel and kept with a
-        per-channel values when dtype is 'image' or 'video'.
+        leading channel axis (e.g. shape (3, 1, 1) for RGB). RGB stats are divided by
+        255 to land in [0, 1]; depth maps (features flagged with ``is_depth_map``) skip
+        this rescaling and remain in their stored units.
     """
     if quantile_list is None:
         quantile_list = DEFAULT_QUANTILES
@@ -531,8 +533,12 @@ def compute_episode_stats(
         )
 
         if features[key]["dtype"] in ["image", "video"]:
+            normalization_factor = (
+                255.0 if not (features[key].get("info") or {}).get("is_depth_map", False) else 1.0
+            )
             ep_stats[key] = {
-                k: v if k == "count" else np.squeeze(v / 255.0, axis=0) for k, v in ep_stats[key].items()
+                k: v if k == "count" else np.squeeze(v / normalization_factor, axis=0)
+                for k, v in ep_stats[key].items()
             }
 
     return ep_stats
@@ -552,8 +558,10 @@ def _validate_stat_value(value: np.ndarray, key: str, feature_key: str) -> None:
     if key == "count" and value.shape != (1,):
         raise ValueError(f"Shape of 'count' must be (1), but is {value.shape} instead.")
 
-    if "image" in feature_key and key != "count" and value.shape != (3, 1, 1):
+    if "image" in feature_key and key != "count" and value.shape not in ((3, 1, 1), (1, 1, 1)):
-        raise ValueError(f"Shape of quantile '{key}' must be (3,1,1), but is {value.shape} instead.")
+        raise ValueError(
+            f"Shape of quantile '{key}' must be (3,1,1) or (1,1,1) but is {value.shape} instead."
+        )
 
 
 def _assert_type_and_shape(stats_list: list[dict[str, dict]]):

src/lerobot/datasets/dataset_metadata.py

@@ -14,7 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import contextlib
-from collections.abc import Callable
+from collections.abc import Callable, Iterable
+from copy import deepcopy
 from pathlib import Path
 
 import numpy as np
@@ -337,6 +338,25 @@ class LeRobotDatasetMetadata:
         """Keys to access visual modalities stored as videos."""
         return [key for key, ft in self.features.items() if ft["dtype"] == "video"]
 
+    @property
+    def depth_keys(self) -> list[str]:
+        """Keys to access depth-map modalities stored as videos or images.
+
+        A depth key is a feature whose ``info`` dict carries ``"is_depth_map": True``
+        (or the legacy ``"video.is_depth_map"`` inside ``info`` or ``video_info``).
+        """
+
+        def _is_depth(ft: dict) -> bool:
+            info = ft.get("info") or {}
+            video_info = ft.get("video_info") or {}
+            return (
+                info.get("is_depth_map", False)
+                or info.get("video.is_depth_map", False)
+                or video_info.get("video.is_depth_map", False)
+            )
+
+        return [key for key, ft in self.features.items() if _is_depth(ft)]
+
     @property
     def camera_keys(self) -> list[str]:
         """Keys to access visual modalities (regardless of their storage method)."""
@@ -580,29 +600,41 @@ class LeRobotDatasetMetadata:
     def update_video_info(
         self,
         video_key: str | None = None,
-        camera_encoder: VideoEncoderConfig | None = None,
+        video_encoder: VideoEncoderConfig | None = None,
+        preserve_keys: Iterable[str] | None = None,
     ) -> None:
-        """Populate per-feature video info in ``info.json``.
+        """Populate or refresh per-feature video info in ``info.json``.
 
         Warning: this function writes info from first episode videos, implicitly assuming that all videos have
         been encoded the same way. Also, this means it assumes the first episode exists.
 
+        Always re-probes the videos and overwrites existing info for every recomputed
+        key. ``preserve_keys`` lists keys whose existing values must be kept (e.g.
+        data-intrinsic entries like ``is_depth_map`` and depth quantization params)
+        instead of being recomputed.
+
         Args:
             video_key: If provided, only update this video key. Otherwise update
                 all video keys in the dataset.
-            camera_encoder: Encoder configuration used to produce the
+            video_encoder: Encoder configuration used to produce the
                 videos. When provided, its fields are recorded as
                 ``video.<field>`` entries alongside the stream-derived
                 ``video.*`` entries (see :func:`get_video_info`).
+            preserve_keys: Keys whose existing values are kept instead of being
+                recomputed. ``None`` (default) recomputes every key.
         """
         if video_key is not None and video_key not in self.video_keys:
             raise ValueError(f"Video key {video_key} not found in dataset")
 
         video_keys = [video_key] if video_key is not None else self.video_keys
+        preserve_set = set(preserve_keys or ())
         for key in video_keys:
-            if not self.features[key].get("info", None):
+            existing = self.features[key].get("info") or {}
-                video_path = self.root / self.video_path.format(video_key=key, chunk_index=0, file_index=0)
+            video_path = self.root / self.video_path.format(video_key=key, chunk_index=0, file_index=0)
-                self.info.features[key]["info"] = get_video_info(video_path, camera_encoder=camera_encoder)
+            new_info = get_video_info(video_path, video_encoder=video_encoder)
+            # Drop preserved keys so the existing values win on merge.
+            new_info = {k: v for k, v in new_info.items() if k not in preserve_set}
+            self.info.features[key]["info"] = {**existing, **new_info}
 
     def update_chunk_settings(
         self,
@@ -709,7 +741,7 @@ class LeRobotDatasetMetadata:
 
         obj.root.mkdir(parents=True, exist_ok=False)
 
-        features = {**features, **DEFAULT_FEATURES}
+        features = {**deepcopy(features), **DEFAULT_FEATURES}
         _validate_feature_names(features)
 
         obj.tasks = None

src/lerobot/datasets/dataset_reader.py

@@ -22,7 +22,10 @@ from pathlib import Path
 import datasets
 import torch
 
+from lerobot.configs.video import DepthEncoderConfig
+
 from .dataset_metadata import LeRobotDatasetMetadata
+from .depth_utils import dequantize_depth
 from .feature_utils import (
     check_delta_timestamps,
     get_delta_indices,
@@ -51,6 +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.
 
@@ -68,6 +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
@@ -76,6 +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
@@ -86,6 +95,12 @@ class DatasetReader:
             check_delta_timestamps(delta_timestamps, meta.fps, tolerance_s)
             self.delta_indices = get_delta_indices(delta_timestamps, meta.fps)
 
+        ##TODO(CarolinePascal): Should we rather use a more lightweight structure ?
+        self._depth_encoder_configs: dict[str, DepthEncoderConfig] = {
+            vid_key: DepthEncoderConfig.from_video_info(self._meta.features[vid_key].get("info"))
+            for vid_key in self._meta.depth_keys
+        }
+
     def try_load(self) -> bool:
         """Attempt to load from local cache. Returns True if data is sufficient."""
         try:
@@ -247,7 +262,18 @@ class DatasetReader:
                 self._tolerance_s,
                 self._video_backend,
                 return_uint8=self._return_uint8,
+                is_depth=vid_key in self._meta.depth_keys,
             )
+            if vid_key in self._meta.depth_keys:
+                depth_encoder = self._depth_encoder_configs[vid_key]
+                frames = dequantize_depth(
+                    frames,
+                    depth_min=depth_encoder.depth_min,
+                    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)
 
         items = list(query_timestamps.items())

src/lerobot/datasets/dataset_tools.py

@@ -27,6 +27,7 @@ import logging
 import shutil
 from collections.abc import Callable
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
+from copy import deepcopy
 from pathlib import Path
 
 import datasets
@@ -36,7 +37,14 @@ import pyarrow.parquet as pq
 import torch
 from tqdm import tqdm
 
-from lerobot.configs import VideoEncoderConfig, camera_encoder_defaults
+from lerobot.configs import (
+    DepthEncoderConfig,
+    VideoEncoderConfig,
+    camera_encoder_defaults,
+    depth_encoder_defaults,
+    encoder_config_from_video_info,
+)
+from lerobot.configs.video import DEPTH_ENCODER_INFO_FIELD_NAMES
 from lerobot.utils.constants import ACTION, HF_LEROBOT_HOME, OBS_IMAGE, OBS_STATE
 from lerobot.utils.utils import flatten_dict
 
@@ -47,6 +55,7 @@ from .compute_stats import (
     compute_relative_action_stats,
 )
 from .dataset_metadata import LeRobotDatasetMetadata
+from .image_writer import write_image
 from .io_utils import (
     get_parquet_file_size_in_mb,
     load_episodes,
@@ -61,12 +70,13 @@ from .utils import (
     DEFAULT_DATA_FILE_SIZE_IN_MB,
     DEFAULT_DATA_PATH,
     DEFAULT_EPISODES_PATH,
+    DEPTH_FILE_PATTERN,
+    IMAGE_FILE_PATTERN,
     VIDEO_DIR,
     update_chunk_file_indices,
 )
 from .video_utils import (
     encode_video_frames,
-    get_video_info,
     reencode_video,
 )
 
@@ -600,7 +610,7 @@ def _keep_episodes_from_video_with_av(
     output_path: Path,
     episodes_to_keep: list[tuple[int, int]],
     fps: float,
-    camera_encoder: VideoEncoderConfig,
+    video_encoder: VideoEncoderConfig,
 ) -> None:
     """Keep only specified episodes from a video file using PyAV.
 
@@ -614,7 +624,7 @@ def _keep_episodes_from_video_with_av(
             Ranges are half-open intervals: [start_frame, end_frame), where start_frame
             is inclusive and end_frame is exclusive.
         fps: Frame rate of the video.
-        camera_encoder: Video encoder settings used to re-encode the kept frames.
+        video_encoder: Video encoder settings used to re-encode the kept frames.
     """
     from fractions import Fraction
 
@@ -639,13 +649,13 @@ def _keep_episodes_from_video_with_av(
 
     # Convert fps to Fraction for PyAV compatibility.
     fps_fraction = Fraction(fps).limit_denominator(1000)
-    codec_options = camera_encoder.get_codec_options(as_strings=True)
+    codec_options = video_encoder.get_codec_options(as_strings=True)
-    v_out = out.add_stream(camera_encoder.vcodec, rate=fps_fraction, options=codec_options)
+    v_out = out.add_stream(video_encoder.vcodec, rate=fps_fraction, options=codec_options)
 
     # PyAV type stubs don't distinguish video streams from audio/subtitle streams.
     v_out.width = v_in.codec_context.width
     v_out.height = v_in.codec_context.height
-    v_out.pix_fmt = camera_encoder.pix_fmt
+    v_out.pix_fmt = video_encoder.pix_fmt
 
     # Set time_base to match the frame rate for proper timestamp handling.
     v_out.time_base = Fraction(1, int(fps))
@@ -732,7 +742,7 @@ def _copy_and_reindex_videos(
 
     for video_key in src_dataset.meta.video_keys:
         logging.info(f"Processing videos for {video_key}")
-        camera_encoder = VideoEncoderConfig.from_video_info(
+        video_encoder = encoder_config_from_video_info(
             src_dataset.meta.info.features.get(video_key, {}).get("info")
         )
 
@@ -816,7 +826,7 @@ def _copy_and_reindex_videos(
                     dst_video_path,
                     episodes_to_keep_ranges,
                     src_dataset.meta.fps,
-                    camera_encoder,
+                    video_encoder,
                 )
 
                 cumulative_ts = 0.0
@@ -1101,7 +1111,9 @@ def _copy_episodes_metadata_and_stats(
     if dst_meta.video_keys and src_dataset.meta.video_keys:
         for key in dst_meta.video_keys:
             if key in src_dataset.meta.features:
-                dst_meta.info.features[key]["info"] = src_dataset.meta.info.features[key].get("info", {})
+                dst_meta.info.features[key]["info"] = deepcopy(
+                    src_dataset.meta.info.features[key].get("info", {})
+                )
 
     write_info(dst_meta.info, dst_meta.root)
 
@@ -1150,15 +1162,15 @@ def _save_episode_images_for_video(
     # Get all items for this episode
     episode_dataset = imgs_dataset.select(range(from_idx, to_idx))
 
+    is_depth = img_key in dataset.meta.depth_keys
+    frame_pattern = DEPTH_FILE_PATTERN if is_depth else IMAGE_FILE_PATTERN
+
     # Define function to save a single image
     def save_single_image(i_item_tuple):
         i, item = i_item_tuple
-        img = item[img_key]
+        write_image(item[img_key], imgs_dir / frame_pattern.format(frame_index=i))
-        # Use frame-XXXXXX.png format to match encode_video_frames expectations
-        img.save(str(imgs_dir / f"frame-{i:06d}.png"), quality=100)
         return i
 
-    # Save images with proper naming convention for encode_video_frames (frame-XXXXXX.png)
     items = list(enumerate(episode_dataset))
 
     with ThreadPoolExecutor(max_workers=num_workers) as executor:
@@ -1190,13 +1202,14 @@ def _save_batch_episodes_images(
     hf_dataset = dataset.hf_dataset.with_format(None)
     imgs_dataset = hf_dataset.select_columns(img_key)
 
+    is_depth = img_key in dataset.meta.depth_keys
+    frame_pattern = DEPTH_FILE_PATTERN if is_depth else IMAGE_FILE_PATTERN
+
     # Define function to save a single image with global frame index
     # Defined once outside the loop to avoid repeated closure creation
     def save_single_image(i_item_tuple, base_frame_idx, img_key_param):
         i, item = i_item_tuple
-        img = item[img_key_param]
+        write_image(item[img_key_param], imgs_dir / frame_pattern.format(frame_index=base_frame_idx + i))
-        # Use global frame index for naming
-        img.save(str(imgs_dir / f"frame-{base_frame_idx + i:06d}.png"), quality=100)
         return i
 
     episode_durations = []
@@ -1287,7 +1300,7 @@ def _estimate_frame_size_via_calibration(
     episode_indices: list[int],
     temp_dir: Path,
     fps: int,
-    camera_encoder: VideoEncoderConfig,
+    video_encoder: VideoEncoderConfig,
     num_calibration_frames: int = 30,
 ) -> float:
     """Estimate MB per frame by encoding a small calibration sample.
@@ -1301,7 +1314,7 @@ def _estimate_frame_size_via_calibration(
         episode_indices: List of episode indices being processed.
         temp_dir: Temporary directory for calibration files.
         fps: Frames per second for video encoding.
-        camera_encoder: Video encoder settings used for calibration encoding.
+        video_encoder: Video encoder settings used for calibration encoding.
         num_calibration_frames: Number of frames to use for calibration (default: 30).
 
     Returns:
@@ -1326,10 +1339,11 @@ def _estimate_frame_size_via_calibration(
         hf_dataset = dataset.hf_dataset.with_format(None)
         sample_indices = range(from_idx, from_idx + num_frames)
 
-        # Save calibration frames
+        # Save calibration frames using the suffix/format the encoder expects.
+        is_depth = img_key in dataset.meta.depth_keys
+        frame_pattern = DEPTH_FILE_PATTERN if is_depth else IMAGE_FILE_PATTERN
         for i, idx in enumerate(sample_indices):
-            img = hf_dataset[idx][img_key]
+            write_image(hf_dataset[idx][img_key], calibration_dir / frame_pattern.format(frame_index=i))
-            img.save(str(calibration_dir / f"frame-{i:06d}.png"), quality=100)
 
         # Encode calibration video
         calibration_video_path = calibration_dir / "calibration.mp4"
@@ -1337,7 +1351,7 @@ def _estimate_frame_size_via_calibration(
             imgs_dir=calibration_dir,
             video_path=calibration_video_path,
             fps=fps,
-            camera_encoder=camera_encoder,
+            video_encoder=video_encoder,
             overwrite=True,
         )
 
@@ -1610,6 +1624,7 @@ def recompute_stats(
         raise ValueError(f"No parquet files found in {data_dir}")
 
     all_episode_stats = []
+    # TODO: enable image and video stats re-computation
     numeric_keys = [k for k, v in features_to_compute.items() if v["dtype"] not in ["image", "video"]]
 
     for parquet_path in tqdm(parquet_files, desc="Computing stats from data files"):
@@ -1656,6 +1671,7 @@ def convert_image_to_video_dataset(
     output_dir: Path | None = None,
     repo_id: str | None = None,
     camera_encoder: VideoEncoderConfig | None = None,
+    depth_encoder: DepthEncoderConfig | None = None,
     episode_indices: list[int] | None = None,
     num_workers: int = 4,
     max_episodes_per_batch: int | None = None,
@@ -1667,21 +1683,32 @@ def convert_image_to_video_dataset(
     LeRobot dataset structure with videos stored in chunked MP4 files.
 
     Args:
-        dataset: The source LeRobot dataset with images
+        dataset: The source LeRobot dataset with images.
-        output_dir: Root directory where the edited dataset will be stored. If not specified, defaults to $HF_LEROBOT_HOME/repo_id. Equivalent to new_root in EditDatasetConfig.
+        output_dir: Root directory where the converted dataset will be stored. When
-        repo_id: Edited dataset identifier. Equivalent to new_repo_id in EditDatasetConfig.
+            ``None``, defaults to ``$HF_LEROBOT_HOME/repo_id``. Equivalent to
-        camera_encoder: Video encoder settings
+            ``new_root`` in ``EditDatasetConfig``.
-            (``None`` uses :func:`~lerobot.configs.camera_encoder_defaults`).
+        repo_id: Converted dataset identifier. Equivalent to ``new_repo_id`` in
-        episode_indices: List of episode indices to convert (None = all episodes)
+            ``EditDatasetConfig``.
-        num_workers: Number of threads for parallel processing (default: 4)
+        camera_encoder: Video encoder settings applied to RGB cameras. When ``None``,
-        max_episodes_per_batch: Maximum episodes per video batch to avoid memory issues (None = no limit)
+            :func:`~lerobot.configs.video.camera_encoder_defaults` is used.
-        max_frames_per_batch: Maximum frames per video batch to avoid memory issues (None = no limit)
+        depth_encoder: Video encoder settings applied to depth-map cameras, including
+            the quantization parameters persisted to the dataset metadata. When
+            ``None``, :func:`~lerobot.configs.video.depth_encoder_defaults` is used.
+        episode_indices: Episode indices to convert. When ``None``, all episodes are
+            converted.
+        num_workers: Number of threads for parallel processing.
+        max_episodes_per_batch: Maximum episodes per video batch, to bound memory use.
+            ``None`` means no limit.
+        max_frames_per_batch: Maximum frames per video batch, to bound memory use.
+            ``None`` means no limit.
 
     Returns:
-        New LeRobotDataset with images encoded as videos
+        A new :class:`LeRobotDataset` with images encoded as videos.
     """
     if camera_encoder is None:
         camera_encoder = camera_encoder_defaults()
+    if depth_encoder is None:
+        depth_encoder = depth_encoder_defaults()
 
     # Check that it's an image dataset
     if len(dataset.meta.video_keys) > 0:
@@ -1706,10 +1733,7 @@ def convert_image_to_video_dataset(
     logging.info(
         f"Converting {len(episode_indices)} episodes with {len(img_keys)} cameras from {dataset.repo_id}"
     )
-    logging.info(
+    logging.info(f"RGB video encoder: {camera_encoder}, depth video encoder: {depth_encoder}")
-        f"Video codec: {camera_encoder.vcodec}, pixel format: {camera_encoder.pix_fmt}, "
-        f"GOP: {camera_encoder.g}, CRF: {camera_encoder.crf}"
-    )
 
     # Create new features dict, converting image features to video features
     new_features = {}
@@ -1771,6 +1795,8 @@ def convert_image_to_video_dataset(
         episode_lengths = {ep_idx: dataset.meta.episodes["length"][ep_idx] for ep_idx in episode_indices}
 
         for img_key in tqdm(img_keys, desc="Processing cameras"):
+            target_encoder = depth_encoder if img_key in dataset.meta.depth_keys else camera_encoder
+
             # Estimate size per frame by encoding a small calibration sample
             # This provides accurate compression ratio for the specific codec parameters
             size_per_frame_mb = _estimate_frame_size_via_calibration(
@@ -1779,7 +1805,7 @@ def convert_image_to_video_dataset(
                 episode_indices=episode_indices,
                 temp_dir=temp_dir,
                 fps=fps,
-                camera_encoder=camera_encoder,
+                video_encoder=target_encoder,
             )
 
             logging.info(f"Processing camera: {img_key}")
@@ -1821,7 +1847,7 @@ def convert_image_to_video_dataset(
                     imgs_dir=imgs_dir,
                     video_path=video_path,
                     fps=fps,
-                    camera_encoder=camera_encoder,
+                    video_encoder=target_encoder,
                     overwrite=True,
                 )
 
@@ -1860,16 +1886,11 @@ def convert_image_to_video_dataset(
         new_meta.info.total_tasks = dataset.meta.total_tasks
         new_meta.info.splits = {"train": f"0:{len(episode_indices)}"}
 
-        # Update video info for all image keys (now videos)
+        # Update video info for all image keys (now videos). They are registered as
-        # We need to manually set video info since update_video_info() checks video_keys first
+        # video features above, so update_video_info populates their (still-empty) info.
         for img_key in img_keys:
-            if not new_meta.features[img_key].get("info", None):
+            target_encoder = depth_encoder if img_key in dataset.meta.depth_keys else camera_encoder
-                video_path = new_meta.root / new_meta.video_path.format(
+            new_meta.update_video_info(video_key=img_key, video_encoder=target_encoder)
-                    video_key=img_key, chunk_index=0, file_index=0
-                )
-                new_meta.info.features[img_key]["info"] = get_video_info(
-                    video_path, camera_encoder=camera_encoder
-                )
 
         write_info(new_meta.info, new_meta.root)
 
@@ -1896,11 +1917,11 @@ def convert_image_to_video_dataset(
 
 def _reencode_video_worker(args: tuple) -> Path:
     """Picklable worker for :func:`reencode_dataset`'s process pool."""
-    video_path, camera_encoder, encoder_threads = args
+    video_path, video_encoder, encoder_threads = args
     reencode_video(
         input_video_path=video_path,
         output_video_path=video_path,
-        camera_encoder=camera_encoder,
+        video_encoder=video_encoder,
         encoder_threads=encoder_threads,
         overwrite=True,
     )
@@ -1909,7 +1930,8 @@ def _reencode_video_worker(args: tuple) -> Path:
 
 def reencode_dataset(
     dataset: LeRobotDataset,
-    camera_encoder: VideoEncoderConfig,
+    camera_encoder: VideoEncoderConfig | None = None,
+    depth_encoder: DepthEncoderConfig | None = None,
     encoder_threads: int | None = None,
     num_workers: int | None = None,
 ) -> LeRobotDataset:
@@ -1920,8 +1942,11 @@ def reencode_dataset(
     Args:
         dataset: An existing :class:`LeRobotDataset` whose videos will be
             re-encoded.
-        camera_encoder: Target encoder configuration applied to every video
+        camera_encoder: Target encoder configuration applied to every RGB video
-            file.
+            file. If ``None``, re-encoding is skipped for RGB videos.
+        depth_encoder: Target encoder configuration applied to every depth video
+            file. If ``None``, re-encoding is skipped for depth videos.
+            Quantization parameters will not override the ones in the current dataset.
         encoder_threads: Per-encoder thread count forwarded to
             :func:`reencode_video`. ``None`` lets the codec decide.
         num_workers: Number of parallel processes. ``None`` or ``0`` means
@@ -1933,23 +1958,35 @@ def reencode_dataset(
         on disk.
     """
     meta = dataset.meta
-    video_paths_list = []
+    video_keys_encoders_dict = {}
+    video_keys_paths_dict = {}
+
+    if camera_encoder is None and depth_encoder is None:
+        raise ValueError("Either camera_encoder or depth_encoder must be provided")
 
     # Only re-encode if the videos are not already encoded with the given video encoding parameters
     for video_key in meta.video_keys:
         current_info = meta.info.features[video_key].get("info", {})
-        current_encoder = VideoEncoderConfig.from_video_info(current_info)
+        current_encoder = encoder_config_from_video_info(current_info)
-        if current_encoder != camera_encoder:
+        target_encoder = depth_encoder if video_key in meta.depth_keys else camera_encoder
-            video_paths_list.extend((meta.root / VIDEO_DIR / video_key).rglob("*.mp4"))
+        if target_encoder is None:
+            logging.info(f"No encoder provided for {video_key} video. Skipping re-encoding.")
+        elif current_encoder != target_encoder:
+            video_keys_paths_dict[video_key] = list((meta.root / VIDEO_DIR / video_key).rglob("*.mp4"))
+            video_keys_encoders_dict[video_key] = target_encoder
         else:
-            logging.info(f"{video_key} videos are already encoded with {camera_encoder}. Nothing to do.")
+            logging.info(f"{video_key} videos are already encoded with {target_encoder}. Nothing to do.")
 
-    if len(video_paths_list) == 0:
+    if len(video_keys_paths_dict) == 0:
         logging.warning("Dataset has no videos to re-encode.")
         return dataset
-    logging.info(f"Re-encoding {len(video_paths_list)} video file(s) with {camera_encoder}")
+    logging.info(f"Re-encoding {sum(len(paths) for paths in video_keys_paths_dict.values())} video file(s).")
 
-    worker_args = [(vp, camera_encoder, encoder_threads) for vp in video_paths_list]
+    worker_args = [
+        (path, encoder, encoder_threads)
+        for video_key, encoder in video_keys_encoders_dict.items()
+        for path in video_keys_paths_dict[video_key]
+    ]
     if num_workers and num_workers > 1:
         with ProcessPoolExecutor(max_workers=num_workers) as pool:
             futures = [pool.submit(_reencode_video_worker, args) for args in worker_args]
@@ -1963,10 +2000,15 @@ def reencode_dataset(
         for args in tqdm(worker_args, desc="Re-encoding videos"):
             _reencode_video_worker(args)
 
-    # Refresh video info in metadata for every video key.
+    # Refresh video info in metadata for every re-encoded key. Re-encoding only
-    for vid_key in meta.video_keys:
+    # changes codec/container params, so for depth videos we preserve ``is_depth_map``
-        video_path = meta.root / meta.get_video_file_path(0, vid_key)
+    # and the depth quantization params (``video.depth_min`` / ``video.depth_max`` /
-        meta.info.features[vid_key]["info"] = get_video_info(video_path, camera_encoder=camera_encoder)
+    # ...), which describe the data rather than the codec and must survive a transcode.
+    # RGB videos pass an empty set: still a refresh, but nothing to preserve.
+    depth_preserve_keys = {"is_depth_map", *(f"video.{n}" for n in DEPTH_ENCODER_INFO_FIELD_NAMES)}
+    for video_key, encoder in video_keys_encoders_dict.items():
+        preserve_keys = depth_preserve_keys if video_key in meta.depth_keys else set()
+        meta.update_video_info(video_key=video_key, video_encoder=encoder, preserve_keys=preserve_keys)
 
     write_info(meta.info, meta.root)
     logging.info("Dataset metadata updated.")

src/lerobot/datasets/dataset_writer.py

@@ -31,7 +31,12 @@ import PIL.Image
 import pyarrow.parquet as pq
 import torch
 
-from lerobot.configs import VideoEncoderConfig, camera_encoder_defaults
+from lerobot.configs import (
+    DepthEncoderConfig,
+    VideoEncoderConfig,
+    camera_encoder_defaults,
+    depth_encoder_defaults,
+)
 
 from .compute_stats import compute_episode_stats
 from .dataset_metadata import LeRobotDatasetMetadata
@@ -48,6 +53,7 @@ from .io_utils import (
     write_info,
 )
 from .utils import (
+    DEFAULT_DEPTH_PATH,
     DEFAULT_EPISODES_PATH,
     DEFAULT_IMAGE_PATH,
     update_chunk_file_indices,
@@ -67,17 +73,22 @@ def _encode_video_worker(
     episode_index: int,
     root: Path,
     fps: int,
-    camera_encoder: VideoEncoderConfig | None = None,
+    video_encoder: VideoEncoderConfig | None = None,
     encoder_threads: int | None = None,
 ) -> Path:
     temp_path = Path(tempfile.mkdtemp(dir=root)) / f"{video_key}_{episode_index:03d}.mp4"
-    fpath = DEFAULT_IMAGE_PATH.format(image_key=video_key, episode_index=episode_index, frame_index=0)
+    path_template = (
+        DEFAULT_DEPTH_PATH
+        if video_encoder is not None and isinstance(video_encoder, DepthEncoderConfig)
+        else DEFAULT_IMAGE_PATH
+    )
+    fpath = path_template.format(image_key=video_key, episode_index=episode_index, frame_index=0)
     img_dir = (root / fpath).parent
     encode_video_frames(
         img_dir,
         temp_path,
         fps,
-        camera_encoder=camera_encoder,
+        video_encoder=video_encoder,
         encoder_threads=encoder_threads,
         overwrite=True,
     )
@@ -97,6 +108,7 @@ class DatasetWriter:
         meta: LeRobotDatasetMetadata,
         root: Path,
         camera_encoder: VideoEncoderConfig | None,
+        depth_encoder: DepthEncoderConfig | None,
         encoder_threads: int | None,
         batch_encoding_size: int,
         streaming_encoder: StreamingVideoEncoder | None = None,
@@ -108,8 +120,11 @@ class DatasetWriter:
             meta: Dataset metadata instance (used for feature schema, chunk
                 settings, and episode persistence).
             root: Local dataset root directory.
-            camera_encoder: Video encoder settings applied to all cameras.
+            camera_encoder: Video encoder settings applied to RGB cameras. When
-                ``None`` uses :func:`~lerobot.configs.camera_encoder_defaults`.
+                ``None``, :func:`~lerobot.configs.video.camera_encoder_defaults` is used.
+            depth_encoder: Video encoder settings applied to depth cameras, including
+                the quantization parameters. When ``None``,
+                :func:`~lerobot.configs.video.depth_encoder_defaults` is used.
             encoder_threads: Number of encoder threads (global). ``None``
                 lets the codec decide.
             batch_encoding_size: Number of episodes to accumulate before
@@ -121,6 +136,7 @@ class DatasetWriter:
         self._meta = meta
         self._root = root
         self._camera_encoder = camera_encoder or camera_encoder_defaults()
+        self._depth_encoder = depth_encoder or depth_encoder_defaults()
         self._encoder_threads = encoder_threads
         self._batch_encoding_size = batch_encoding_size
         self._streaming_encoder = streaming_encoder
@@ -145,7 +161,8 @@ class DatasetWriter:
         return ep_buffer
 
     def _get_image_file_path(self, episode_index: int, image_key: str, frame_index: int) -> Path:
-        fpath = DEFAULT_IMAGE_PATH.format(
+        path_template = DEFAULT_DEPTH_PATH if image_key in self._meta.depth_keys else DEFAULT_IMAGE_PATH
+        fpath = path_template.format(
             image_key=image_key, episode_index=episode_index, frame_index=frame_index
         )
         return self._root / fpath
@@ -195,6 +212,7 @@ class DatasetWriter:
         if frame_index == 0 and self._streaming_encoder is not None:
             self._streaming_encoder.start_episode(
                 video_keys=list(self._meta.video_keys),
+                depth_video_keys=list(self._meta.depth_keys),
                 temp_dir=self._root,
             )
 
@@ -282,10 +300,13 @@ class DatasetWriter:
         if use_streaming:
             streaming_results = self._streaming_encoder.finish_episode()
             for video_key in self._meta.video_keys:
+                normalization_factor = 255.0 if video_key not in self._meta.depth_keys else 1.0
                 temp_path, video_stats = streaming_results[video_key]
                 if video_stats is not None:
                     ep_stats[video_key] = {
-                        k: v if k == "count" else np.squeeze(v.reshape(1, -1, 1, 1) / 255.0, axis=0)
+                        k: v
+                        if k == "count"
+                        else np.squeeze(v.reshape(1, -1, 1, 1) / normalization_factor, axis=0)
                         for k, v in video_stats.items()
                     }
                 ep_metadata.update(self._save_episode_video(video_key, episode_index, temp_path=temp_path))
@@ -300,7 +321,9 @@ class DatasetWriter:
                             episode_index,
                             self._root,
                             self._meta.fps,
-                            self._camera_encoder,
+                            self._depth_encoder
+                            if video_key in self._meta.depth_keys
+                            else self._camera_encoder,
                             self._encoder_threads,
                         ): video_key
                         for video_key in self._meta.video_keys
@@ -511,7 +534,12 @@ class DatasetWriter:
 
         # Update video info (only needed when first episode is encoded)
         if episode_index == 0:
-            self._meta.update_video_info(video_key, camera_encoder=self._camera_encoder)
+            self._meta.update_video_info(
+                video_key,
+                video_encoder=self._depth_encoder
+                if video_key in self._meta.depth_keys
+                else self._camera_encoder,
+            )
             write_info(self._meta.info, self._meta.root)
 
         metadata = {
@@ -578,13 +606,14 @@ class DatasetWriter:
             self.image_writer.wait_until_done()
 
     def _encode_temporary_episode_video(self, video_key: str, episode_index: int) -> Path:
-        """Use ffmpeg to convert frames stored as png into mp4 videos."""
+        """Use ffmpeg to convert frames stored as png/tiff into mp4 videos."""
+        is_depth = video_key in self._meta.depth_keys
         return _encode_video_worker(
             video_key,
             episode_index,
             self._root,
             self._meta.fps,
-            self._camera_encoder,
+            self._depth_encoder if is_depth else self._camera_encoder,
             self._encoder_threads,
         )
 

src/lerobot/datasets/depth_utils.py

@@ -0,0 +1,256 @@
+#!/usr/bin/env python
+
+# 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.
+"""
+Depth encoding/decoding helpers for :class:`VideoEncoderConfig`.
+"""
+
+import math
+from typing import Literal
+
+import av
+import numpy as np
+import torch
+from numpy.typing import NDArray
+
+from lerobot.configs.video import (
+    DEFAULT_DEPTH_MAX,
+    DEFAULT_DEPTH_MIN,
+    DEFAULT_DEPTH_PIX_FMT,
+    DEFAULT_DEPTH_SHIFT,
+    DEFAULT_DEPTH_USE_LOG,
+    DEPTH_QMAX,
+)
+
+from .pyav_utils import write_u16_plane
+
+_MM_PER_METRE = 1000.0
+_UINT16_MAX = 65535
+
+
+def _validate_log_quant_params(depth_min: float, shift: float) -> None:
+    """Ensure ``log(depth_min + shift)`` is finite."""
+    if depth_min + shift <= 0:
+        raise ValueError(
+            f"depth_min + shift must be positive for logarithmic quantization, "
+            f"got depth_min={depth_min} + shift={shift} = {depth_min + shift}"
+        )
+
+
+def _depth_input_to_float32_and_unit(
+    depth: NDArray[np.integer] | NDArray[np.floating],
+    input_unit: Literal["auto", "m", "mm"],
+) -> tuple[NDArray[np.float32], Literal["m", "mm"]]:
+    """Convert depth to float32 in the chosen unit, and return the resolved unit."""
+    resolved_unit = (
+        ("m" if np.issubdtype(depth.dtype, np.floating) else "mm") if input_unit == "auto" else input_unit
+    )
+    return depth.astype(np.float32, order="K"), resolved_unit
+
+
+def quantize_depth(
+    depth: NDArray[np.uint16] | NDArray[np.float32] | torch.Tensor,
+    depth_min: float = DEFAULT_DEPTH_MIN,
+    depth_max: float = DEFAULT_DEPTH_MAX,
+    shift: float = DEFAULT_DEPTH_SHIFT,
+    use_log: bool = DEFAULT_DEPTH_USE_LOG,
+    pix_fmt: str = DEFAULT_DEPTH_PIX_FMT,
+    video_backend: str | None = "pyav",
+    input_unit: Literal["auto", "m", "mm"] = "auto",
+) -> NDArray[np.uint16] | av.VideoFrame:
+    """Quantize depth to 12-bit codes (``uint16``, values ``0…DEPTH_QMAX``).
+
+    Depth maps are packed into 12-bit integer frames so they fit in standard
+    high-bit-depth pixel formats (e.g. ``yuv420p12le`` / ``gray12le``)
+    and can be encoded by widely supported video codecs (HEVC Main 12, ffv1).
+    Logarithmic quantization is the default because it allocates more quanta
+    to near-range depth, which matches the (1/depth) error profile of typical
+    depth sensors. Math is ported from BEHAVIOR-1K's ``obs_utils.py``.
+
+    **Input units**:
+
+    - ``input_unit="auto"`` (default): infer from dtype (floating = m, non-floating = mm).
+    - ``input_unit="mm"``: interpret input values as millimetres.
+    - ``input_unit="m"``: interpret input values as metres.
+
+    Quantization math runs in the **resolved input unit**.
+
+    ``depth_min``, ``depth_max``, and ``shift`` are always in **metres**.
+
+    Args:
+        depth: Depth map; ``torch.Tensor`` is moved to CPU for conversion.
+        depth_min: Depth (metres) at quantum ``0``.
+        depth_max: Depth (metres) at quantum :data:`DEPTH_QMAX`.
+        shift: Depth shift (metres); used in log mode. Must satisfy ``depth_min + shift > 0``.
+        use_log: If ``True`` (default), quantize in log space.
+        video_backend: Video backend to use for encoding. Defaults to "pyav".
+        input_unit: Input unit policy (``"auto"``, ``"mm"``, ``"m"``).
+
+    Returns:
+        ``numpy.ndarray``, ``dtype=uint16``, same shape as ``depth``, values in
+        ``[0, DEPTH_QMAX]``.
+
+    Raises:
+        ValueError: If ``input_unit`` is not ``"auto"``, ``"mm"``, or ``"m"``.
+        ValueError: If ``use_log=True`` and ``depth_min + shift <= 0``.
+    """
+    if input_unit not in ("auto", "m", "mm"):
+        raise ValueError(f"input_unit must be 'auto', 'm', or 'mm', got {input_unit!r}")
+
+    if isinstance(depth, torch.Tensor):
+        depth = depth.detach().cpu().numpy()
+
+    # Squeeze single-channel dim: (H, W, 1) or (1, H, W) → (H, W)
+    if depth.ndim == 3 and (depth.shape[-1] == 1 or depth.shape[0] == 1):
+        depth = depth.squeeze()
+
+    depth_f, resolved_unit = _depth_input_to_float32_and_unit(depth, input_unit=input_unit)
+
+    # Convert depth_min, depth_max, and shift to the resolved input unit.
+    depth_min_u = np.float32(depth_min) if resolved_unit == "m" else np.float32(depth_min * _MM_PER_METRE)
+    depth_max_u = np.float32(depth_max) if resolved_unit == "m" else np.float32(depth_max * _MM_PER_METRE)
+    shift_u = np.float32(shift) if resolved_unit == "m" else np.float32(shift * _MM_PER_METRE)
+
+    # Normalization and quantization is performed in the resolved input unit.
+    if use_log:
+        _validate_log_quant_params(depth_min, shift)
+        log_min = math.log(float(depth_min_u + shift_u))
+        log_max = math.log(float(depth_max_u + shift_u))
+        norm = (np.log(depth_f + shift_u) - log_min) / (log_max - log_min)
+    else:
+        norm = (depth_f - depth_min_u) / (depth_max_u - depth_min_u)
+
+    quantized = np.rint(norm * DEPTH_QMAX).clip(0, DEPTH_QMAX).astype(np.uint16, copy=False)
+
+    if video_backend == "pyav":
+        frame = av.VideoFrame.from_ndarray(quantized, format=pix_fmt)
+        write_u16_plane(frame.planes[0], quantized)
+        return frame
+    else:
+        return quantized
+
+
+def dequantize_depth(
+    quantized: NDArray[np.uint16] | av.VideoFrame | torch.Tensor,
+    depth_min: float = DEFAULT_DEPTH_MIN,
+    depth_max: float = DEFAULT_DEPTH_MAX,
+    shift: float = DEFAULT_DEPTH_SHIFT,
+    use_log: bool = DEFAULT_DEPTH_USE_LOG,
+    pix_fmt: str = DEFAULT_DEPTH_PIX_FMT,
+    output_unit: Literal["m", "mm"] = "mm",
+    output_tensor: bool = True,
+    output_channel_last: bool = False,
+) -> NDArray[np.uint16] | NDArray[np.float32] | torch.Tensor:
+    """Inverse of :func:`quantize_depth`.
+
+    Decoding inverts the same normalized code mapping as :func:`quantize_depth`
+    using ``depth_min`` / ``depth_max`` / ``shift`` (in metres), then returns
+    the requested output unit. Tuning arguments **must match** :func:`quantize_depth`.
+
+    Accepted input layouts :
+
+    - ``(H, W, 1)`` or ``(H, W)`` — single frame with channel-last.
+    - ``(..., 1, H, W)`` — batched frames with channel-first.
+    - ``(..., H, W, 1)`` — batched frames with channel-last.
+    Output layout is determined by ``output_channel_last``.
+
+    Args:
+        quantized: 12-bit codes in ``[0, DEPTH_QMAX]``. ``np.ndarray``,
+            ``av.VideoFrame``, or ``torch.Tensor`` (any integer or float dtype).
+        depth_min, depth_max, shift, use_log: Same as :func:`quantize_depth` (metres).
+        pix_fmt: Pixel format used to extract the plane from an ``av.VideoFrame``.
+        output_unit: ``"mm"`` returns ``uint16`` millimetres (rint, clip
+            ``[0, 65535]``) when returning a numpy array, or ``float32`` mm when
+            ``output_tensor=True``. ``"m"`` returns ``float32`` metres in
+            ``[depth_min, depth_max]``.
+        output_tensor: If True, return a ``torch.Tensor`` instead of a numpy array.
+
+    Returns:
+        Depth map in the requested unit and dtype.
+
+    Raises:
+        ValueError: If ``output_unit`` is not ``"m"`` or ``"mm"``.
+        ValueError: If ``use_log=True`` and ``depth_min + shift <= 0``.
+    """
+    if output_unit not in ("m", "mm"):
+        raise ValueError(f"output_unit must be 'm' or 'mm', got {output_unit!r}")
+    if use_log:
+        _validate_log_quant_params(depth_min, shift)
+
+    if isinstance(quantized, av.VideoFrame):
+        quantized = quantized.to_ndarray(format=pix_fmt)
+
+    # Compute the scale and offset first.
+    depth_min_m = float(depth_min)
+    depth_max_m = float(depth_max)
+    shift_m = float(shift)
+    if use_log:
+        log_min = math.log(depth_min_m + shift_m)
+        log_max = math.log(depth_max_m + shift_m)
+        scale = (log_max - log_min) / DEPTH_QMAX
+        offset = log_min
+    else:
+        scale = (depth_max_m - depth_min_m) / DEPTH_QMAX
+        offset = depth_min_m
+
+    # ── Torch path: stay on the input device, single fp32 allocation. ────────
+    if isinstance(quantized, torch.Tensor):
+        if quantized.ndim >= 3:
+            # Drop the single-channel dimension so the math runs on (..., H, W).
+            quantized = quantized.squeeze(-3) if quantized.shape[-3] == 1 else quantized.squeeze(-1)
+
+        # Single allocation we own; everything else is in-place.
+        buf = quantized.to(dtype=torch.float32, copy=True)
+        buf.mul_(scale).add_(offset)
+        if use_log:
+            buf.exp_().sub_(shift_m)
+        buf.clamp_(depth_min_m, depth_max_m)
+        buf.unsqueeze_(-1) if output_channel_last else buf.unsqueeze_(-3)
+
+        if output_unit == "m":
+            return buf if output_tensor else buf.cpu().numpy()
+
+        # mm path: round + clamp in float32, skipping the uint16 round-trip
+        # when returning a tensor (torch.uint16 is poorly supported).
+        buf.mul_(_MM_PER_METRE).round_().clamp_(0.0, _UINT16_MAX)
+        if output_tensor:
+            return buf
+        return buf.cpu().numpy().astype(np.uint16, copy=False)
+
+    # ── NumPy path: single fp32 allocation, ``out=`` for in-place math. ─────
+    arr = np.asarray(quantized)
+    if arr.ndim >= 3:
+        # Drop the single-channel dimension so the math runs on (..., H, W).
+        arr = np.squeeze(arr, axis=-3) if arr.shape[-3] == 1 else np.squeeze(arr, axis=-1)
+
+    buf = np.empty(arr.shape, dtype=np.float32)
+    np.multiply(arr, scale, out=buf)
+    np.add(buf, offset, out=buf)
+    if use_log:
+        np.exp(buf, out=buf)
+        np.subtract(buf, shift_m, out=buf)
+    np.clip(buf, depth_min_m, depth_max_m, out=buf)
+    buf = np.expand_dims(buf, axis=-1) if output_channel_last else np.expand_dims(buf, axis=-3)
+
+    if output_unit == "m":
+        return torch.from_numpy(buf) if output_tensor else buf
+
+    np.multiply(buf, _MM_PER_METRE, out=buf)
+    np.rint(buf, out=buf)
+    np.clip(buf, 0.0, _UINT16_MAX, out=buf)
+    if output_tensor:
+        # torch.uint16 support is very limited; return float32 millimetres.
+        return torch.from_numpy(buf)
+    return buf.astype(np.uint16, copy=False)

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/feature_utils.py

@@ -336,7 +336,7 @@ def validate_feature_image_or_video(
 
     Args:
         name (str): The name of the feature.
-        expected_shape (list[str]): The expected shape (C, H, W).
+        expected_shape (list[str]): The expected shape, e.g. (C, H, W) or (H, W, C).
         value: The image data to validate.
 
     Returns:

src/lerobot/datasets/image_writer.py

@@ -42,10 +42,41 @@ def safe_stop_image_writer(func):
 
 
 def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True) -> PIL.Image.Image:
-    # TODO(aliberts): handle 1 channel and 4 for depth images
+    """Convert a NumPy array to a PIL Image, preserving precision for grayscale.
-    if image_array.ndim != 3:
-        raise ValueError(f"The array has {image_array.ndim} dimensions, but 3 is expected for an image.")
 
+    Behaviour by shape:
+
+    - ``(H, W)`` or ``(1, H, W)`` / ``(H, W, 1)``: single-channel grayscale.
+      The native dtype is preserved using the matching PIL mode
+      (``I;16`` / ``F``). This is the path used for raw depth maps (no rescaling, clamping, or downcasting)
+    - ``(3, H, W)`` / ``(H, W, 3)``: RGB. Channels-first inputs are transposed
+      to channels-last. Float inputs in ``[0, 1]`` are scaled to ``uint8``
+      (existing behaviour, gated by ``range_check``).
+
+    Other shapes / channel counts raise ``NotImplementedError`` or
+    ``ValueError``.
+    """
+    # TODO(CarolinePascal): 4 dimensions RGB-D images
+    if image_array.ndim not in (2, 3):
+        raise ValueError(f"The array has {image_array.ndim} dimensions, but 2 or 3 is expected for an image.")
+
+    # Squeeze 3D single-channel inputs to 2D so depth maps work whether the
+    # caller emits (H, W), (1, H, W), or (H, W, 1).
+    if image_array.ndim == 3:
+        if image_array.shape[0] == 1:
+            image_array = image_array[0]
+        elif image_array.shape[-1] == 1:
+            image_array = image_array[..., 0]
+
+    if image_array.ndim == 2:
+        if image_array.dtype not in [np.uint16, np.float32]:
+            raise ValueError(
+                f"Unsupported single-channel image dtype: {image_array.dtype}. "
+                f"Supported dtypes: {sorted(str(d) for d in [np.uint16, np.float32])}."
+            )
+        return PIL.Image.fromarray(np.ascontiguousarray(image_array))
+
+    # 3D path: must be RGB (3 channels), channels-first or channels-last.
     if image_array.shape[0] == 3:
         # Transpose from pytorch convention (C, H, W) to (H, W, C)
         image_array = image_array.transpose(1, 2, 0)
@@ -71,13 +102,28 @@ def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True)
     return PIL.Image.fromarray(image_array)
 
 
+def save_kwargs_for_path(fpath: Path, compress_level: int) -> dict:
+    """Pick the right format-specific kwargs for :meth:`PIL.Image.Image.save`.
+
+    PNG uses ``compress_level`` (0-9, zlib). TIFF uses ``compression`` (raw) for lossless raw depth maps.
+    """
+    suffix = Path(fpath).suffix.lower()
+    if suffix == ".png":
+        return {"compress_level": compress_level}
+    if suffix in (".tif", ".tiff"):
+        return {"compression": "raw"}
+    return {}
+
+
 def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level: int = 1):
     """
     Saves a NumPy array or PIL Image to a file.
 
     This function handles both NumPy arrays and PIL Image objects, converting
     the former to a PIL Image before saving. It includes error handling for
-    the save operation.
+    the save operation. The output format is inferred from the *fpath*
+    extension: ``.png`` → PNG with ``compress_level``, ``.tiff`` / ``.tif``
+    → lossless raw depth maps (TIFF).
 
     Args:
         image (np.ndarray | PIL.Image.Image): The image data to save.
@@ -101,7 +147,7 @@ def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level
             img = image
         else:
             raise TypeError(f"Unsupported image type: {type(image)}")
-        img.save(fpath, compress_level=compress_level)
+        img.save(fpath, **save_kwargs_for_path(fpath, compress_level))
     except Exception as e:
         logger.error("Error writing image %s: %s", fpath, e)
 

src/lerobot/datasets/io_utils.py

@@ -20,6 +20,7 @@ import datasets
 import numpy as np
 import pandas
 import pandas as pd
+import pyarrow as pa
 import pyarrow.dataset as pa_ds
 import pyarrow.parquet as pq
 import torch
@@ -153,7 +154,7 @@ def cast_stats_to_numpy(stats: dict) -> dict[str, dict[str, np.ndarray]]:
     Returns:
         dict: The statistics dictionary with values cast to numpy arrays.
     """
-    stats = {key: np.atleast_1d(np.array(value)) for key, value in flatten_dict(stats).items()}
+    stats = {key: np.array(value) for key, value in flatten_dict(stats).items()}
     return unflatten_dict(stats)
 
 
@@ -270,21 +271,49 @@ def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[to
     return items_dict
 
 
+def write_table_one_row_group_per_episode(table: pa.Table, path: Path) -> None:
+    """Write ``table`` with one parquet row group per episode (in episode order).
+
+    Keeps shards random-access friendly (``read_row_group(i)`` fetches episode i),
+    mirroring the recording writer. ``table`` must carry a contiguous
+    ``episode_index`` column.
+    """
+    episode_index = table.column("episode_index").to_numpy(zero_copy_only=False)
+    starts = np.concatenate(([0], np.nonzero(np.diff(episode_index))[0] + 1))
+    writer = pq.ParquetWriter(str(path), table.schema, compression="snappy", use_dictionary=True)
+    try:
+        for start, stop in zip(starts, np.append(starts[1:], len(episode_index)), strict=True):
+            writer.write_table(table.slice(start, stop - start))  # one episode -> one row group
+    finally:
+        writer.close()
+
+
 def to_parquet_with_hf_images(
     df: pandas.DataFrame, path: Path, features: datasets.Features | None = None
 ) -> None:
-    """This function correctly writes to parquet a panda DataFrame that contains images encoded by HF dataset.
+    """Write a DataFrame with HF-encoded images to parquet, one row group per episode.
-    This way, it can be loaded by HF dataset and correctly formatted images are returned.
 
-    Args:
+    Images are embedded into the arrow table first (``ParquetWriter.write_table``
-        df: DataFrame to write to parquet.
+    does not embed external image files like ``Dataset.to_parquet`` does).
-        path: Path to write the parquet file.
+    ``features`` types image columns as ``Image()`` in the parquet schema.
-        features: Optional HuggingFace Features schema. If provided, ensures image columns
-                  are properly typed as Image() in the parquet schema.
     """
-    # TODO(qlhoest): replace this weird synthax by `df.to_parquet(path)` only
     ds = datasets.Dataset.from_dict(df.to_dict(orient="list"), features=features)
-    ds.to_parquet(path)
+    ds = embed_images(ds)
+    table = ds.with_format("arrow")[:]
+    if "episode_index" in table.column_names:
+        write_table_one_row_group_per_episode(table, path)
+    else:
+        # No episode boundaries to align row groups to — keep a single write.
+        pq.write_table(table, str(path))
+
+
+def to_parquet_one_row_group_per_episode(df: pandas.DataFrame, path: Path) -> None:
+    """Write a (non-image) DataFrame to parquet with one row group per episode."""
+    table = pa.Table.from_pandas(df, preserve_index=False)
+    if "episode_index" in table.column_names:
+        write_table_one_row_group_per_episode(table, path)
+    else:
+        pq.write_table(table, str(path))
 
 
 def item_to_torch(item: dict) -> dict:

src/lerobot/datasets/lerobot_dataset.py

@@ -24,7 +24,7 @@ import torch.utils
 from huggingface_hub import HfApi, snapshot_download
 from huggingface_hub.errors import RevisionNotFoundError
 
-from lerobot.configs import VideoEncoderConfig
+from lerobot.configs import DepthEncoderConfig, VideoEncoderConfig
 from lerobot.utils.constants import HF_LEROBOT_HUB_CACHE
 
 from .dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
@@ -58,8 +58,10 @@ 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,
         encoder_threads: int | None = None,
         streaming_encoding: bool = False,
         encoder_queue_maxsize: int = 30,
@@ -186,6 +188,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
             camera_encoder (VideoEncoderConfig | None, optional): Video encoder settings for cameras
                 (codec, quality, etc.). When ``None``, :func:`~lerobot.configs.video.camera_encoder_defaults`
                 is used by the writer.
+            depth_encoder (DepthEncoderConfig | None, optional): Video encoder settings for depth cameras
+                (codec, quality, etc.). When ``None``, :func:`~lerobot.configs.video.depth_encoder_defaults`
+                is used by the writer.
             encoder_threads (int | None, optional): Number of encoder threads (global). ``None`` lets the
                 codec decide.
             streaming_encoding (bool, optional): If True, encode video frames in real-time during capture
@@ -208,6 +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
 
@@ -248,6 +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
@@ -273,6 +280,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 streaming_enc = self._build_streaming_encoder(
                     self.meta.fps,
                     camera_encoder,
+                    depth_encoder,
                     encoder_queue_maxsize,
                     encoder_threads,
                 )
@@ -280,6 +288,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 meta=self.meta,
                 root=self.root,
                 camera_encoder=camera_encoder,
+                depth_encoder=depth_encoder,
                 encoder_threads=encoder_threads,
                 batch_encoding_size=batch_encoding_size,
                 streaming_encoder=streaming_enc,
@@ -315,6 +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
 
@@ -322,12 +332,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def _build_streaming_encoder(
         fps: int,
         camera_encoder: VideoEncoderConfig | None,
+        depth_encoder: DepthEncoderConfig | None,
         encoder_queue_maxsize: int,
         encoder_threads: int | None,
     ) -> StreamingVideoEncoder:
         return StreamingVideoEncoder(
             fps=fps,
             camera_encoder=camera_encoder,
+            depth_encoder=depth_encoder,
             queue_maxsize=encoder_queue_maxsize,
             encoder_threads=encoder_threads,
         )
@@ -646,6 +658,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         video_backend: str | None = None,
         batch_encoding_size: int = 1,
         camera_encoder: VideoEncoderConfig | None = None,
+        depth_encoder: DepthEncoderConfig | None = None,
         metadata_buffer_size: int = 10,
         streaming_encoding: bool = False,
         encoder_queue_maxsize: int = 30,
@@ -678,6 +691,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 batch-encoding videos. ``1`` means encode immediately.
             camera_encoder: Video encoder settings for cameras (codec, quality, etc.).
                 When ``None``, :func:`~lerobot.configs.video.camera_encoder_defaults` is used.
+            depth_encoder: Video encoder settings for depth cameras (codec, quality, etc.).
+                When ``None``, :func:`~lerobot.configs.video.depth_encoder_defaults` is used.
             encoder_threads: Number of encoder threads (global). ``None``
                 lets the codec decide.
             metadata_buffer_size: Number of episode metadata records to buffer
@@ -712,6 +727,7 @@ 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
 
@@ -721,12 +737,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
         streaming_enc = None
         if streaming_encoding and len(obj.meta.video_keys) > 0:
             streaming_enc = cls._build_streaming_encoder(
-                fps, camera_encoder, encoder_queue_maxsize, encoder_threads
+                fps, camera_encoder, depth_encoder, encoder_queue_maxsize, encoder_threads
             )
         obj.writer = DatasetWriter(
             meta=obj.meta,
             root=obj.root,
             camera_encoder=camera_encoder,
+            depth_encoder=depth_encoder,
             encoder_threads=encoder_threads,
             batch_encoding_size=batch_encoding_size,
             streaming_encoder=streaming_enc,
@@ -750,6 +767,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         video_backend: str | None = None,
         batch_encoding_size: int = 1,
         camera_encoder: VideoEncoderConfig | None = None,
+        depth_encoder: DepthEncoderConfig | None = None,
         encoder_threads: int | None = None,
         image_writer_processes: int = 0,
         image_writer_threads: int = 0,
@@ -779,6 +797,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 batch-encoding videos.
             camera_encoder: Video encoder settings for cameras (codec, quality, etc.).
                 When ``None``, :func:`~lerobot.configs.video.camera_encoder_defaults` is used.
+            depth_encoder: Video encoder settings for depth cameras (codec, quality, etc.).
+                When ``None``, :func:`~lerobot.configs.video.depth_encoder_defaults` is used.
             encoder_threads: Number of encoder threads (global). ``None``
                 lets the codec decide.
             image_writer_processes: Subprocesses for async image writing.
@@ -806,6 +826,7 @@ 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:
@@ -825,12 +846,13 @@ class LeRobotDataset(torch.utils.data.Dataset):
         streaming_enc = None
         if streaming_encoding and len(obj.meta.video_keys) > 0:
             streaming_enc = cls._build_streaming_encoder(
-                obj.meta.fps, camera_encoder, encoder_queue_maxsize, encoder_threads
+                obj.meta.fps, camera_encoder, depth_encoder, encoder_queue_maxsize, encoder_threads
             )
         obj.writer = DatasetWriter(
             meta=obj.meta,
             root=obj.root,
             camera_encoder=camera_encoder,
+            depth_encoder=depth_encoder,
             encoder_threads=encoder_threads,
             batch_encoding_size=batch_encoding_size,
             streaming_encoder=streaming_enc,

src/lerobot/datasets/pipeline_features.py

@@ -70,19 +70,21 @@ def aggregate_pipeline_dataset_features(
     initial_features: dict[PipelineFeatureType, dict[str, Any]],
     *,
     use_videos: bool = True,
+    exclude_images: bool = False,
     patterns: Sequence[str] | None = None,
 ) -> dict[str, dict]:
     """
     Aggregates and filters pipeline features to create a dataset-ready features dictionary.
 
     This function transforms initial features using the pipeline, categorizes them as action or observations
-    (image or state), filters them based on `use_videos` and `patterns`, and finally
+    (image or state), filters them based on `exclude_images` and `patterns`, and finally
     formats them for use with a Hugging Face LeRobot Dataset.
 
     Args:
         pipeline: The DataProcessorPipeline to apply.
         initial_features: A dictionary of raw feature specs for actions and observations.
-        use_videos: If False, image features are excluded.
+        use_videos: Controls the storage dtype for image features. If True, images are stored as "video"; if False, they are stored as "image".
+        exclude_images: If True, image features are dropped entirely from the output.
         patterns: A sequence of regex patterns to filter action and state features.
                   Image features are not affected by this filter.
 
@@ -120,7 +122,7 @@ def aggregate_pipeline_dataset_features(
             )
 
             # 2. Apply filtering rules.
-            if is_image and not use_videos:
+            if is_image and exclude_images:
                 continue
             if not is_image and not should_keep(key, compiled_patterns):
                 continue

src/lerobot/datasets/pyav_utils.py

@@ -24,6 +24,7 @@ import logging
 from typing import Any
 
 import av
+import numpy as np
 
 logger = logging.getLogger(__name__)
 
@@ -31,6 +32,22 @@ FFMPEG_NUMERIC_OPTION_TYPES = ("INT", "INT64", "UINT64", "FLOAT", "DOUBLE")
 FFMPEG_INTEGER_OPTION_TYPES = ("INT", "INT64", "UINT64")
 
 
+def write_u16_plane(plane: av.video.plane.VideoPlane, src: np.ndarray, fill_value: int | None = None) -> None:
+    """Copy ``src`` into a uint16 plane respecting FFmpeg line padding."""
+    height, width = src.shape
+    stride_u16 = plane.line_size // np.dtype(np.uint16).itemsize
+    dst = np.frombuffer(plane, dtype=np.uint16).reshape(height, stride_u16)
+    if fill_value is not None:
+        dst.fill(fill_value)
+    dst[:, :width] = src
+
+
+@functools.cache
+def get_pix_fmt_channels(pix_fmt: str) -> int:
+    """Return the number of components (channels) for *pix_fmt*."""
+    return len(av.VideoFormat(pix_fmt).components)
+
+
 @functools.cache
 def get_codec(vcodec: str) -> av.codec.Codec | None:
     """PyAV write-mode ``Codec`` for *vcodec*, or ``None`` if unavailable."""
@@ -92,7 +109,7 @@ def _check_option_value(vcodec: str, label: str, value: Any, opt: av.option.Opti
                     f"{label}={value!r} is not numeric; codec {vcodec!r} expects a number for this option."
                 ) from e
         elif isinstance(value, (float, int)):
-            num_val = value
+            num_val = float(value)
         else:
             raise ValueError(
                 f"{label}={value!r} is not numeric; codec {vcodec!r} expects a number for this option."
@@ -142,6 +159,16 @@ def _check_pixel_format(vcodec: str, pix_fmt: str) -> None:
         )
 
 
+def _check_pix_fmt_channels(pix_fmt: str, channels: int) -> None:
+    """Ensure *pix_fmt* can carry at least *channels* components."""
+    pix_fmt_channels = get_pix_fmt_channels(pix_fmt)
+    if pix_fmt_channels < channels:
+        raise ValueError(
+            f"pix_fmt={pix_fmt!r} carries only {pix_fmt_channels} component(s) "
+            f"but the source data has {channels} channel(s)."
+        )
+
+
 def _check_codec_options(vcodec: str, codec_options: dict[str, Any]) -> None:
     """Validate merged encoder options (typed) against the codec's published AVOptions."""
     supported_options = _get_codec_options_by_name(vcodec)
@@ -156,12 +183,18 @@ def _check_codec_options(vcodec: str, codec_options: dict[str, Any]) -> None:
         _check_option_value(vcodec, key, value, supported_options[key])
 
 
-def check_video_encoder_parameters_pyav(vcodec: str, pix_fmt: str, codec_options: dict[str, Any]) -> None:
+def check_video_encoder_parameters_pyav(
+    vcodec: str,
+    pix_fmt: str,
+    codec_options: dict[str, Any],
+    channels: int | None = None,
+) -> None:
     """Verify *config* is compatible with the bundled FFmpeg build.
 
     Checks pixel format, abstract tuning-field compatibility, and each merged
     encoder option from :meth:`~lerobot.configs.video.VideoEncoderConfig.get_codec_options`
     against PyAV (including numeric ``extra_options`` present in that dict).
+    When given, additionally verify that *pix_fmt* carries as many components as the source data channels.
     No-op when ``config.vcodec`` isn't in the local FFmpeg build.
 
     Raises:
@@ -171,4 +204,6 @@ def check_video_encoder_parameters_pyav(vcodec: str, pix_fmt: str, codec_options
     if not options:
         raise ValueError(f"Codec {vcodec!r} is not available in the bundled FFmpeg build")
     _check_pixel_format(vcodec, pix_fmt)
+    if channels is not None:
+        _check_pix_fmt_channels(pix_fmt, channels)
     _check_codec_options(vcodec, codec_options)

src/lerobot/datasets/utils.py

@@ -87,11 +87,14 @@ DATA_DIR = "data"
 VIDEO_DIR = "videos"
 
 CHUNK_FILE_PATTERN = "chunk-{chunk_index:03d}/file-{file_index:03d}"
+IMAGE_FILE_PATTERN = "frame-{frame_index:06d}.png"
+DEPTH_FILE_PATTERN = "frame-{frame_index:06d}.tiff"
 DEFAULT_TASKS_PATH = "meta/tasks.parquet"
 DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_DATA_PATH = DATA_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_VIDEO_PATH = VIDEO_DIR + "/{video_key}/" + CHUNK_FILE_PATTERN + ".mp4"
-DEFAULT_IMAGE_PATH = "images/{image_key}/episode-{episode_index:06d}/frame-{frame_index:06d}.png"
+DEFAULT_IMAGE_PATH = "images/{image_key}/episode-{episode_index:06d}/" + IMAGE_FILE_PATTERN
+DEFAULT_DEPTH_PATH = "images/{image_key}/episode-{episode_index:06d}/" + DEPTH_FILE_PATTERN
 
 LEGACY_EPISODES_PATH = "meta/episodes.jsonl"
 LEGACY_EPISODES_STATS_PATH = "meta/episodes_stats.jsonl"

src/lerobot/datasets/video_utils.py

@@ -39,11 +39,16 @@ from datasets.features.features import register_feature
 from PIL import Image
 
 from lerobot.configs import (
+    DepthEncoderConfig,
     VideoEncoderConfig,
     camera_encoder_defaults,
+    depth_encoder_defaults,
 )
 from lerobot.utils.import_utils import get_safe_default_video_backend
 
+from .depth_utils import quantize_depth
+from .pyav_utils import get_pix_fmt_channels
+
 logger = logging.getLogger(__name__)
 
 
@@ -53,6 +58,7 @@ def decode_video_frames(
     tolerance_s: float,
     backend: str | None = None,
     return_uint8: bool = False,
+    is_depth: bool = False,
 ) -> torch.Tensor:
     """
     Decodes video frames using the specified backend.
@@ -64,23 +70,35 @@ def decode_video_frames(
         backend (str, optional): Backend to use for decoding. Defaults to "torchcodec" when available
             in the platform; otherwise, defaults to "pyav". The legacy value "video_reader" is
             accepted for one release as an alias for "pyav" and will be removed in a future version.
-        return_uint8 (bool): If True, return raw uint8 frames without float32 normalization.
+        return_uint8 (bool): For RGB videos, if True return raw uint8 frames without float32 normalization.
             This reduces memory for DataLoader IPC; normalization can be done on GPU afterward.
+        is_depth (bool): Set to True if the video is a depth map (1 channel, uint12).
 
     Returns:
-        torch.Tensor: Decoded frames (float32 in [0,1] by default, or uint8 if return_uint8=True).
+        torch.Tensor: Decoded frames (RGB: float32 in [0,1] by default, or uint8 if return_uint8=True, Depth: uint12).
 
     Currently supports torchcodec on cpu and pyav.
     """
+    if backend != "pyav" and is_depth:
+        logger.warning("Decoding depth maps is only supported with the 'pyav' backend.")
+        # We do not actually return uint8 here, but we avoid the 255 normalization step.
+        return decode_video_frames_pyav(
+            video_path, timestamps, tolerance_s, return_uint8=False, is_depth=True
+        )
+
     if backend is None:
         backend = get_safe_default_video_backend()
     if backend == "torchcodec":
         return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s, return_uint8=return_uint8)
     elif backend == "pyav":
-        return decode_video_frames_pyav(video_path, timestamps, tolerance_s, return_uint8=return_uint8)
+        return decode_video_frames_pyav(
+            video_path, timestamps, tolerance_s, return_uint8=return_uint8, is_depth=is_depth
+        )
     elif backend == "video_reader":
         logger.warning("backend='video_reader' is deprecated and now aliases to 'pyav'.")
-        return decode_video_frames_pyav(video_path, timestamps, tolerance_s, return_uint8=return_uint8)
+        return decode_video_frames_pyav(
+            video_path, timestamps, tolerance_s, return_uint8=return_uint8, is_depth=is_depth
+        )
     else:
         raise ValueError(f"Unsupported video backend: {backend}")
 
@@ -91,6 +109,7 @@ def decode_video_frames_pyav(
     tolerance_s: float,
     log_loaded_timestamps: bool = False,
     return_uint8: bool = False,
+    is_depth: bool = False,
 ) -> torch.Tensor:
     """Loads frames associated to the requested timestamps of a video using PyAV.
 
@@ -109,8 +128,9 @@ def decode_video_frames_pyav(
         tolerance_s: Allowed deviation in seconds between a queried timestamp and the closest
             decoded frame.
         log_loaded_timestamps: When True, log every decoded frame's timestamp at INFO level.
-        return_uint8: When True, return raw uint8 frames (C, H, W). Otherwise, return float32 in
+        return_uint8: For RGB videos, if True return raw uint8 frames (C, H, W).
-            [0, 1] range.
+            Otherwise, return float32 in [0, 1] range.
+        is_depth: Set to True if the video is a depth map (1 channel, uint12).
 
     Returns:
         torch.Tensor of shape (len(timestamps), C, H, W).
@@ -140,9 +160,13 @@ def decode_video_frames_pyav(
             current_ts = float(frame.pts * stream.time_base)
             if log_loaded_timestamps:
                 logger.info(f"frame loaded at timestamp={current_ts:.4f}")
-            # Convert to CHW uint8 to match torchcodec's output layout.
+            if is_depth:
-            arr = frame.to_ndarray(format="rgb24")  # H, W, 3
+                arr = frame.to_ndarray(format="gray12le")  # (H, W) uint12
-            loaded_frames.append(torch.from_numpy(arr).permute(2, 0, 1).contiguous())
+                loaded_frames.append(torch.from_numpy(arr).unsqueeze(0).contiguous())
+            else:
+                arr = frame.to_ndarray(format="rgb24")  # (H, W, 3)
+                # Convert to CHW uint8 to match torchcodec's output layout.
+                loaded_frames.append(torch.from_numpy(arr).permute(2, 0, 1).contiguous())
             loaded_ts.append(current_ts)
             if current_ts >= last_ts:
                 break
@@ -185,7 +209,7 @@ def decode_video_frames_pyav(
             f"number of queried timestamps ({len(timestamps)})"
         )
 
-    if return_uint8:
+    if return_uint8 or is_depth:
         return closest_frames
 
     # convert to the pytorch format which is float32 in [0,1] range (and channel first)
@@ -406,17 +430,38 @@ def encode_video_frames(
     imgs_dir: Path | str,
     video_path: Path | str,
     fps: int,
-    camera_encoder: VideoEncoderConfig | None = None,
+    video_encoder: VideoEncoderConfig | None = None,
     encoder_threads: int | None = None,
     *,
     log_level: int | None = av.logging.WARNING,
     overwrite: bool = False,
 ) -> None:
-    """More info on ffmpeg arguments tuning on `benchmark/video/README.md`"""
+    """Encode a directory of image frames into an MP4 video.
-    if camera_encoder is None:
+
-        camera_encoder = camera_encoder_defaults()
+    When ``video_encoder`` is a :class:`~lerobot.configs.video.DepthEncoderConfig`,
-    vcodec = camera_encoder.vcodec
+    frames are read from ``.tiff`` files and quantized to 12-bit depth codes using the
-    pix_fmt = camera_encoder.pix_fmt
+    encoder's ``depth_min`` / ``depth_max`` / ``shift`` / ``use_log``; otherwise ``.png``
+    RGB frames are encoded directly.
+
+    Args:
+        imgs_dir: Directory containing the frames to encode, named ``frame-000000``
+            onwards (``.png`` for RGB, ``.tiff`` for depth).
+        video_path: Output path for the encoded ``.mp4`` file.
+        fps: Frame rate of the output video.
+        video_encoder: Encoder settings (codec, pixel format, quality, ...). When
+            ``None``, :func:`camera_encoder_defaults` is used. Pass a
+            :class:`~lerobot.configs.video.DepthEncoderConfig` to encode depth frames.
+        encoder_threads: Per-encoder thread count forwarded to the codec. ``None``
+            lets the codec decide.
+        log_level: libav log level to set while encoding, or ``None`` to leave the
+            current logging configuration unchanged.
+        overwrite: When ``False`` and ``video_path`` already exists, skip encoding and
+            log a warning. When ``True``, re-encode and replace the existing file.
+    """
+    if video_encoder is None:
+        video_encoder = camera_encoder_defaults()
+    vcodec = video_encoder.vcodec
+    pix_fmt = video_encoder.pix_fmt
 
     video_path = Path(video_path)
     imgs_dir = Path(imgs_dir)
@@ -428,17 +473,19 @@ def encode_video_frames(
     video_path.parent.mkdir(parents=True, exist_ok=True)
 
     # Get input frames
-    template = "frame-" + ("[0-9]" * 6) + ".png"
+    is_depth = isinstance(video_encoder, DepthEncoderConfig)
+    suffix = ".png" if not is_depth else ".tiff"
+    template = "frame-" + ("[0-9]" * 6) + suffix
     input_list = sorted(
         glob.glob(str(imgs_dir / template)), key=lambda x: int(x.split("-")[-1].split(".")[0])
     )
 
     if len(input_list) == 0:
-        raise FileNotFoundError(f"No images found in {imgs_dir}.")
+        raise FileNotFoundError(f"No images with suffix {suffix} found in {imgs_dir}.")
     with Image.open(input_list[0]) as dummy_image:
         width, height = dummy_image.size
 
-    video_options = camera_encoder.get_codec_options(encoder_threads, as_strings=True)
+    video_options = video_encoder.get_codec_options(encoder_threads, as_strings=True)
 
     # Set logging level
     if log_level is not None:
@@ -455,8 +502,19 @@ def encode_video_frames(
         # Loop through input frames and encode them
         for input_data in input_list:
             with Image.open(input_data) as input_image:
-                input_image = input_image.convert("RGB")
+                if is_depth:
-                input_frame = av.VideoFrame.from_image(input_image)
+                    input_frame = quantize_depth(
+                        np.array(input_image),
+                        depth_min=video_encoder.depth_min,
+                        depth_max=video_encoder.depth_max,
+                        shift=video_encoder.shift,
+                        use_log=video_encoder.use_log,
+                        pix_fmt=video_encoder.pix_fmt,
+                        video_backend="pyav",
+                    )
+                else:
+                    input_image = input_image.convert("RGB")
+                    input_frame = av.VideoFrame.from_image(input_image)
                 packet = output_stream.encode(input_frame)
                 if packet:
                     output.mux(packet)
@@ -477,7 +535,7 @@ def encode_video_frames(
 def reencode_video(
     input_video_path: Path | str,
     output_video_path: Path | str,
-    camera_encoder: VideoEncoderConfig | None = None,
+    video_encoder: VideoEncoderConfig | None = None,
     encoder_threads: int | None = None,
     log_level: int | None = av.logging.WARNING,
     overwrite: bool = False,
@@ -489,7 +547,7 @@ def reencode_video(
     Args:
         input_video_path: Existing video file to read.
         output_video_path: Path for the re-encoded file.
-        camera_encoder: Encoder configuration. Defaults to :func:`camera_encoder_defaults`.
+        video_encoder: Encoder configuration. Defaults to :func:`camera_encoder_defaults`.
         encoder_threads: Optional thread count forwarded to :meth:`VideoEncoderConfig.get_codec_options`.
         log_level: libav log level while encoding, or ``None`` to leave logging unchanged. Defaults to WARNING.
         overwrite: When ``False`` and ``output_video_path`` already exists, skip and log a warning.
@@ -497,7 +555,7 @@ def reencode_video(
         end_time_s: When set, trim the output to end at this timestamp (seconds, exclusive).
     """
 
-    camera_encoder = camera_encoder or camera_encoder_defaults()
+    video_encoder = video_encoder or camera_encoder_defaults()
 
     if (start_time_s is not None and start_time_s < 0) or (end_time_s is not None and end_time_s < 0):
         raise ValueError(f"Trim times must be non-negative, got start={start_time_s}, end={end_time_s}.")
@@ -512,9 +570,9 @@ def reencode_video(
 
     output_video_path.parent.mkdir(parents=True, exist_ok=True)
 
-    video_options = camera_encoder.get_codec_options(encoder_threads, as_strings=True)
+    video_options = video_encoder.get_codec_options(encoder_threads, as_strings=True)
-    vcodec = camera_encoder.vcodec
+    vcodec = video_encoder.vcodec
-    pix_fmt = camera_encoder.pix_fmt
+    pix_fmt = video_encoder.pix_fmt
 
     with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_named_file:
         tmp_output_video_path = tmp_named_file.name
@@ -696,22 +754,21 @@ class _CameraEncoderThread(threading.Thread):
         self,
         video_path: Path,
         fps: int,
-        vcodec: str,
+        video_encoder: VideoEncoderConfig,
-        pix_fmt: str,
-        codec_options: dict[str, str],
         frame_queue: queue.Queue,
         result_queue: queue.Queue,
         stop_event: threading.Event,
+        encoder_threads: int | None = None,
     ):
         super().__init__(daemon=True)
         self.video_path = video_path
         self.fps = fps
-        self.vcodec = vcodec
+        self.video_encoder = video_encoder
-        self.pix_fmt = pix_fmt
+        self.is_depth = isinstance(video_encoder, DepthEncoderConfig)
-        self.codec_options = codec_options
         self.frame_queue = frame_queue
         self.result_queue = result_queue
         self.stop_event = stop_event
+        self.encoder_threads = encoder_threads
 
     def run(self) -> None:
         from .compute_stats import RunningQuantileStats, auto_downsample_height_width
@@ -736,12 +793,12 @@ class _CameraEncoderThread(threading.Thread):
                     # Sentinel: flush and close
                     break
 
-                # Ensure HWC uint8 numpy array
+                # Ensure HWC (RGB or depth) uint8 (RGB only) numpy array
                 if isinstance(frame_data, np.ndarray):
-                    if frame_data.ndim == 3 and frame_data.shape[0] == 3:
+                    if frame_data.ndim == 3 and frame_data.shape[0] in (1, 3):
                         # CHW -> HWC
                         frame_data = frame_data.transpose(1, 2, 0)
-                    if frame_data.dtype != np.uint8:
+                    if not self.is_depth and frame_data.dtype != np.uint8:
                         frame_data = (frame_data * 255).astype(np.uint8)
 
                 # Open container on first frame (to get width/height)
@@ -749,15 +806,29 @@ class _CameraEncoderThread(threading.Thread):
                     height, width = frame_data.shape[:2]
                     Path(self.video_path).parent.mkdir(parents=True, exist_ok=True)
                     container = av.open(str(self.video_path), "w")
-                    output_stream = container.add_stream(self.vcodec, self.fps, options=self.codec_options)
+                    output_stream = container.add_stream(
-                    output_stream.pix_fmt = self.pix_fmt
+                        self.video_encoder.vcodec,
+                        self.fps,
+                        options=self.video_encoder.get_codec_options(self.encoder_threads, as_strings=True),
+                    )
+                    output_stream.pix_fmt = self.video_encoder.pix_fmt
                     output_stream.width = width
                     output_stream.height = height
                     output_stream.time_base = Fraction(1, self.fps)
 
                 # Encode frame with explicit timestamps
-                pil_img = Image.fromarray(frame_data)
+                if not self.is_depth:
-                video_frame = av.VideoFrame.from_image(pil_img)
+                    pil_img = Image.fromarray(frame_data)
+                    video_frame = av.VideoFrame.from_image(pil_img)
+                else:
+                    video_frame = quantize_depth(
+                        frame_data,
+                        depth_min=self.video_encoder.depth_min,
+                        depth_max=self.video_encoder.depth_max,
+                        shift=self.video_encoder.shift,
+                        use_log=self.video_encoder.use_log,
+                        video_backend=self.video_encoder.video_backend,
+                    )
                 video_frame.pts = frame_count
                 video_frame.time_base = Fraction(1, self.fps)
                 packet = output_stream.encode(video_frame)
@@ -816,21 +887,26 @@ class StreamingVideoEncoder:
         self,
         fps: int,
         camera_encoder: VideoEncoderConfig | None = None,
+        depth_encoder: DepthEncoderConfig | None = None,
         queue_maxsize: int = 30,
         encoder_threads: int | None = None,
     ):
         """
         Args:
             fps: Frames per second for the output videos.
-            camera_encoder: Video encoder settings applied to all cameras.
+            camera_encoder: Video encoder settings applied to all RGB cameras.
                 When ``None``, :func:`camera_encoder_defaults` is used.
-            encoder_threads: Number of encoder threads (global setting).
+            depth_encoder: Video encoder settings applied to all depth cameras,
-                ``None`` lets the codec decide.
+                including the depth quantization parameters. When ``None``,
+                :func:`depth_encoder_defaults` is used.
             queue_maxsize: Max frames to buffer per camera before
                 back-pressure drops frames.
+            encoder_threads: Number of encoder threads (global setting).
+                ``None`` lets the codec decide.
         """
         self.fps = fps
         self._camera_encoder = camera_encoder or camera_encoder_defaults()
+        self._depth_encoder = depth_encoder or depth_encoder_defaults()
         self._encoder_threads = encoder_threads
         self.queue_maxsize = queue_maxsize
 
@@ -843,18 +919,25 @@ class StreamingVideoEncoder:
         self._episode_active = False
         self._closed = False
 
-    def start_episode(self, video_keys: list[str], temp_dir: Path) -> None:
+    def start_episode(
+        self, video_keys: list[str], temp_dir: Path, depth_video_keys: list[str] | None = None
+    ) -> None:
         """Start encoder threads for a new episode.
 
         Args:
             video_keys: List of video feature keys (e.g. ["observation.images.laptop"])
             temp_dir: Base directory for temporary MP4 files
+            depth_video_keys: List of video or image feature keys that carry depth maps (e.g.
+                ["observation.images.laptop_depth"]).  Defaults to ``[]`` (no depth keys).
         """
         if self._episode_active:
             self.cancel_episode()
 
         self._dropped_frames.clear()
 
+        if depth_video_keys is None:
+            depth_video_keys = []
+
         for video_key in video_keys:
             frame_queue: queue.Queue = queue.Queue(maxsize=self.queue_maxsize)
             result_queue: queue.Queue = queue.Queue(maxsize=1)
@@ -863,17 +946,15 @@ class StreamingVideoEncoder:
             temp_video_dir = Path(tempfile.mkdtemp(dir=temp_dir))
             video_path = temp_video_dir / f"{video_key.replace('/', '_')}_streaming.mp4"
 
-            vcodec = self._camera_encoder.vcodec
+            encoder = self._depth_encoder if video_key in depth_video_keys else self._camera_encoder
-            codec_options = self._camera_encoder.get_codec_options(self._encoder_threads, as_strings=True)
             encoder_thread = _CameraEncoderThread(
                 video_path=video_path,
                 fps=self.fps,
-                vcodec=vcodec,
+                video_encoder=encoder,
-                pix_fmt=self._camera_encoder.pix_fmt,
-                codec_options=codec_options,
                 frame_queue=frame_queue,
                 result_queue=result_queue,
                 stop_event=stop_event,
+                encoder_threads=self._encoder_threads,
             )
             encoder_thread.start()
 
@@ -1080,15 +1161,23 @@ def get_audio_info(video_path: Path | str) -> dict:
 
 def get_video_info(
     video_path: Path | str,
-    camera_encoder: VideoEncoderConfig | None = None,
+    video_encoder: VideoEncoderConfig | None = None,
 ) -> dict:
     """Build the ``video.*`` / ``audio.*`` info dict persisted in ``info.json``.
 
     Args:
         video_path: Path to the encoded video file to probe.
-        camera_encoder: If provided, record the exact encoder settings used to encode this
+        video_encoder: If provided, record the exact encoder settings used to encode this
             video. Stream-derived values take precedence — encoder fields are only written for keys
-            not already populated from the video file itself.
+            not already populated from the video file itself. When a
+            :class:`~lerobot.configs.video.DepthEncoderConfig` is passed, the depth
+            quantization parameters (``depth_min`` / ``depth_max`` / ``shift`` /
+            ``use_log``) are recorded so frames can be dequantized on read.
+
+    Returns:
+        The ``video.*`` / ``audio.*`` info dict, including ``is_depth_map`` which is
+        ``True`` only when ``video_encoder`` is a
+        :class:`~lerobot.configs.video.DepthEncoderConfig`.
     """
     logging.getLogger("libav").setLevel(av.logging.WARNING)
 
@@ -1106,13 +1195,10 @@ def get_video_info(
         video_info["video.width"] = video_stream.width
         video_info["video.codec"] = video_stream.codec.canonical_name
         video_info["video.pix_fmt"] = video_stream.pix_fmt
-        video_info["video.is_depth_map"] = False
 
         # Calculate fps from r_frame_rate
         video_info["video.fps"] = int(video_stream.base_rate)
-
+        video_info["video.channels"] = get_pix_fmt_channels(video_stream.pix_fmt)
-        pixel_channels = get_video_pixel_channels(video_stream.pix_fmt)
-        video_info["video.channels"] = pixel_channels
 
     # Reset logging level
     av.logging.restore_default_callback()
@@ -1121,27 +1207,18 @@ def get_video_info(
     video_info.update(**get_audio_info(video_path))
 
     # Add additional encoder configuration if provided
-    if camera_encoder is not None:
+    if video_encoder is not None:
-        for field_name, field_value in asdict(camera_encoder).items():
+        for field_name, field_value in asdict(video_encoder).items():
             # vcodec is already populated from the video stream
             if field_name == "vcodec":
                 continue
             video_info.setdefault(f"video.{field_name}", field_value)
 
+    video_info["is_depth_map"] = isinstance(video_encoder, DepthEncoderConfig)
+
     return video_info
 
 
-def get_video_pixel_channels(pix_fmt: str) -> int:
-    if "gray" in pix_fmt or "depth" in pix_fmt or "monochrome" in pix_fmt:
-        return 1
-    elif "rgba" in pix_fmt or "yuva" in pix_fmt:
-        return 4
-    elif "rgb" in pix_fmt or "yuv" in pix_fmt:
-        return 3
-    else:
-        raise ValueError("Unknown format")
-
-
 def get_video_duration_in_s(video_path: Path | str) -> float:
     """
     Get the duration of a video file in seconds using PyAV.
@@ -1202,10 +1279,13 @@ class VideoEncodingManager:
         img_dir = self.dataset.root / "images"
         if img_dir.exists():
             png_files = list(img_dir.rglob("*.png"))
-            if len(png_files) == 0:
+            tiff_files = list(img_dir.rglob("*.tiff"))
+            if len(png_files) == 0 and len(tiff_files) == 0:
                 shutil.rmtree(img_dir)
                 logger.debug("Cleaned up empty images directory")
             else:
-                logger.debug(f"Images directory is not empty, containing {len(png_files)} PNG files")
+                logger.debug(
+                    f"Images directory is not empty, containing {len(png_files)} PNG and {len(tiff_files)} TIFF files"
+                )
 
         return False  # Don't suppress the original exception

src/lerobot/policies/utils.py

@@ -126,7 +126,8 @@ def prepare_observation_for_inference(
     for name in observation:
         observation[name] = torch.from_numpy(observation[name])
         if "image" in name:
-            observation[name] = observation[name].type(torch.float32) / 255
+            if observation[name].dtype == torch.uint8:
+                observation[name] = observation[name].type(torch.float32) / 255
             observation[name] = observation[name].permute(2, 0, 1).contiguous()
         observation[name] = observation[name].unsqueeze(0)
         observation[name] = observation[name].to(device)

src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py

@@ -18,7 +18,8 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..openarm_follower import OpenArmFollower, OpenArmFollowerConfig
 from ..robot import Robot
@@ -27,7 +28,7 @@ from .config_bi_openarm_follower import BiOpenArmFollowerConfig
 logger = logging.getLogger(__name__)
 
 
-class BiOpenArmFollower(Robot):
+class BiOpenArmFollower(BimanualMixin, Robot):
     """
     Bimanual OpenArm Follower Arms
     """
@@ -39,15 +40,17 @@ class BiOpenArmFollower(Robot):
         super().__init__(config)
         self.config = config
 
-        # Top-level cameras are distributed evenly: each arm's OpenArmFollower
+        # Top-level cameras are opened by `left_arm` for convenience, but their
-        # will only open the cameras assigned to it. Per-arm cameras are used
+        # keys stay unprefixed in observations (tracked via `_top_level_cam_keys`).
-        # as fallback when top-level cameras are empty.
+        self._top_level_cam_keys = set(config.cameras)
-        if config.cameras:
+        _collisions = self._top_level_cam_keys & set(
-            left_cameras = config.cameras
+            config.left_arm_config.cameras
-            right_cameras = {}
+        ) | self._top_level_cam_keys & set(config.right_arm_config.cameras)
-        else:
+        if _collisions:
-            left_cameras = config.left_arm_config.cameras
+            raise ValueError(
-            right_cameras = config.right_arm_config.cameras
+                f"Top-level camera names collide with per-arm camera names: {sorted(_collisions)}"
+            )
+        left_arm_cameras = {**config.left_arm_config.cameras, **config.cameras}
 
         left_arm_config = OpenArmFollowerConfig(
             id=f"{config.id}_left" if config.id else None,
@@ -56,7 +59,7 @@ class BiOpenArmFollower(Robot):
             disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
             use_velocity_and_torque=config.left_arm_config.use_velocity_and_torque,
             max_relative_target=config.left_arm_config.max_relative_target,
-            cameras=left_cameras,
+            cameras=left_arm_cameras,
             side=config.left_arm_config.side,
             can_interface=config.left_arm_config.can_interface,
             use_can_fd=config.left_arm_config.use_can_fd,
@@ -75,7 +78,7 @@ class BiOpenArmFollower(Robot):
             disable_torque_on_disconnect=config.right_arm_config.disable_torque_on_disconnect,
             use_velocity_and_torque=config.right_arm_config.use_velocity_and_torque,
             max_relative_target=config.right_arm_config.max_relative_target,
-            cameras=right_cameras,
+            cameras=config.right_arm_config.cameras,
             side=config.right_arm_config.side,
             can_interface=config.right_arm_config.can_interface,
             use_can_fd=config.right_arm_config.use_can_fd,
@@ -95,22 +98,19 @@ class BiOpenArmFollower(Robot):
 
     @property
     def _motors_ft(self) -> dict[str, type]:
-        left_arm_motors_ft = self.left_arm._motors_ft
-        right_arm_motors_ft = self.right_arm._motors_ft
-
-        # Right first, then left — matches the teleoperator (OpenArmMini) ordering
-        # and the dataset feature names recorded during data collection.
         return {
-            **{f"right_{k}": v for k, v in right_arm_motors_ft.items()},
+            **{f"left_{k}": v for k, v in self.left_arm._motors_ft.items()},
-            **{f"left_{k}": v for k, v in left_arm_motors_ft.items()},
+            **{f"right_{k}": v for k, v in self.right_arm._motors_ft.items()},
         }
 
     @property
     def _cameras_ft(self) -> dict[str, tuple]:
-        # Cameras already have unique user-chosen names (e.g. "left_wrist", "base",
+        out: dict[str, tuple] = {}
-        # "right_wrist"), so we merge them directly — unlike motors which need the
+        for k, v in self.left_arm._cameras_ft.items():
-        # left_/right_ prefix to disambiguate identical per-arm joint names.
+            out[k if k in self._top_level_cam_keys else f"left_{k}"] = v
-        return {**self.left_arm._cameras_ft, **self.right_arm._cameras_ft}
+        for k, v in self.right_arm._cameras_ft.items():
+            out[f"right_{k}"] = v
+        return out
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -120,27 +120,6 @@ class BiOpenArmFollower(Robot):
     def action_features(self) -> dict[str, type]:
         return self._motors_ft
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     def setup_motors(self) -> None:
         raise NotImplementedError(
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
@@ -148,21 +127,15 @@ class BiOpenArmFollower(Robot):
 
     @check_if_not_connected
     def get_observation(self) -> RobotObservation:
-        obs_dict = {}
+        obs_dict: RobotObservation = {}
 
-        # Camera keys that should NOT get the arm prefix (they already have unique names)
+        # Add "left_" prefix to per-arm keys; keep top-level camera keys unprefixed.
-        left_cam_keys = set(self.left_arm.cameras.keys())
+        for key, value in self.left_arm.get_observation().items():
-        right_cam_keys = set(self.right_arm.cameras.keys())
+            obs_dict[key if key in self._top_level_cam_keys else f"left_{key}"] = value
 
-        # Right first, then left — matches the teleoperator (OpenArmMini) ordering
+        # Add "right_" prefix
-        # and the dataset feature names recorded during data collection.
+        for key, value in self.right_arm.get_observation().items():
-        right_obs = self.right_arm.get_observation()
+            obs_dict[f"right_{key}"] = value
-        for key, value in right_obs.items():
-            obs_dict[key if key in right_cam_keys else f"right_{key}"] = value
-
-        left_obs = self.left_arm.get_observation()
-        for key, value in left_obs.items():
-            obs_dict[key if key in left_cam_keys else f"left_{key}"] = value
 
         return obs_dict
 
@@ -189,9 +162,4 @@ class BiOpenArmFollower(Robot):
         prefixed_sent_action_left = {f"left_{key}": value for key, value in sent_action_left.items()}
         prefixed_sent_action_right = {f"right_{key}": value for key, value in sent_action_right.items()}
 
-        return {**prefixed_sent_action_right, **prefixed_sent_action_left}
+        return {**prefixed_sent_action_left, **prefixed_sent_action_right}
-
-    @check_if_not_connected
-    def disconnect(self):
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/robots/bi_openarm_follower/config_bi_openarm_follower.py

@@ -32,5 +32,7 @@ class BiOpenArmFollowerConfig(RobotConfig):
     left_arm_config: OpenArmFollowerConfigBase
     right_arm_config: OpenArmFollowerConfigBase
 
-    # Top-level cameras shared across both arms.
+    # Top-level cameras not attached to a specific side. Keys are kept as-is in
+    # observations (no `left_`/`right_` prefix). Per-arm cameras (declared on
+    # `{left,right}_arm_config.cameras`) are prefixed.
     cameras: dict[str, CameraConfig] = field(default_factory=dict)

src/lerobot/robots/bi_rebot_b601_follower/bi_rebot_b601_follower.py

@@ -18,7 +18,8 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..rebot_b601_follower import RebotB601Follower, RebotB601FollowerRobotConfig
 from ..robot import Robot
@@ -27,7 +28,7 @@ from .config_bi_rebot_b601_follower import BiRebotB601FollowerConfig
 logger = logging.getLogger(__name__)
 
 
-class BiRebotB601Follower(Robot):
+class BiRebotB601Follower(BimanualMixin, Robot):
     """Bimanual Seeed Studio reBot B601-DM follower.
 
     Composes two single-arm :class:`RebotB601Follower` instances. Observation and
@@ -41,6 +42,18 @@ class BiRebotB601Follower(Robot):
         super().__init__(config)
         self.config = config
 
+        # Top-level cameras are opened by `left_arm` for convenience, but their
+        # keys stay unprefixed in observations (tracked via `_top_level_cam_keys`).
+        self._top_level_cam_keys = set(config.cameras)
+        _collisions = self._top_level_cam_keys & set(
+            config.left_arm_config.cameras
+        ) | self._top_level_cam_keys & set(config.right_arm_config.cameras)
+        if _collisions:
+            raise ValueError(
+                f"Top-level camera names collide with per-arm camera names: {sorted(_collisions)}"
+            )
+        left_arm_cameras = {**config.left_arm_config.cameras, **config.cameras}
+
         left_arm_config = RebotB601FollowerRobotConfig(
             id=f"{config.id}_left" if config.id else None,
             calibration_dir=config.calibration_dir,
@@ -49,7 +62,7 @@ class BiRebotB601Follower(Robot):
             dm_serial_baud=config.left_arm_config.dm_serial_baud,
             disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
             max_relative_target=config.left_arm_config.max_relative_target,
-            cameras=config.left_arm_config.cameras,
+            cameras=left_arm_cameras,
             motor_can_ids=config.left_arm_config.motor_can_ids,
             pos_vel_velocity=config.left_arm_config.pos_vel_velocity,
             gripper_torque_ratio=config.left_arm_config.gripper_torque_ratio,
@@ -86,10 +99,12 @@ class BiRebotB601Follower(Robot):
 
     @property
     def _cameras_ft(self) -> dict[str, tuple]:
-        return {
+        out: dict[str, tuple] = {}
-            **{f"left_{k}": v for k, v in self.left_arm._cameras_ft.items()},
+        for k, v in self.left_arm._cameras_ft.items():
-            **{f"right_{k}": v for k, v in self.right_arm._cameras_ft.items()},
+            out[k if k in self._top_level_cam_keys else f"left_{k}"] = v
-        }
+        for k, v in self.right_arm._cameras_ft.items():
+            out[f"right_{k}"] = v
+        return out
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -99,32 +114,13 @@ class BiRebotB601Follower(Robot):
     def action_features(self) -> dict[str, type]:
         return self._motors_ft
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     @check_if_not_connected
     def get_observation(self) -> RobotObservation:
-        obs_dict = {}
+        obs_dict: RobotObservation = {}
-        obs_dict.update({f"left_{k}": v for k, v in self.left_arm.get_observation().items()})
+        for k, v in self.left_arm.get_observation().items():
-        obs_dict.update({f"right_{k}": v for k, v in self.right_arm.get_observation().items()})
+            obs_dict[k if k in self._top_level_cam_keys else f"left_{k}"] = v
+        for k, v in self.right_arm.get_observation().items():
+            obs_dict[f"right_{k}"] = v
         return obs_dict
 
     @check_if_not_connected
@@ -143,8 +139,3 @@ class BiRebotB601Follower(Robot):
             **{f"left_{k}": v for k, v in sent_action_left.items()},
             **{f"right_{k}": v for k, v in sent_action_right.items()},
         }
-
-    @check_if_not_connected
-    def disconnect(self) -> None:
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/robots/bi_rebot_b601_follower/config_bi_rebot_b601_follower.py

@@ -14,7 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
+
+from lerobot.cameras import CameraConfig
 
 from ..config import RobotConfig
 from ..rebot_b601_follower import RebotB601FollowerConfig
@@ -27,3 +29,8 @@ class BiRebotB601FollowerConfig(RobotConfig):
 
     left_arm_config: RebotB601FollowerConfig
     right_arm_config: RebotB601FollowerConfig
+
+    # Top-level cameras not attached to a specific side. Keys are kept as-is in
+    # observations (no `left_`/`right_` prefix). Per-arm cameras (declared on
+    # `{left,right}_arm_config.cameras`) are prefixed.
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)

src/lerobot/robots/bi_so_follower/bi_so_follower.py

@@ -18,7 +18,8 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..robot import Robot
 from ..so_follower import SOFollower, SOFollowerRobotConfig
@@ -27,7 +28,7 @@ from .config_bi_so_follower import BiSOFollowerConfig
 logger = logging.getLogger(__name__)
 
 
-class BiSOFollower(Robot):
+class BiSOFollower(BimanualMixin, Robot):
     """
     [Bimanual SO Follower Arms](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio
     """
@@ -39,6 +40,18 @@ class BiSOFollower(Robot):
         super().__init__(config)
         self.config = config
 
+        # Top-level cameras are opened by `left_arm` for convenience, but their
+        # keys stay unprefixed in observations (tracked via `_top_level_cam_keys`).
+        self._top_level_cam_keys = set(config.cameras)
+        _collisions = self._top_level_cam_keys & set(
+            config.left_arm_config.cameras
+        ) | self._top_level_cam_keys & set(config.right_arm_config.cameras)
+        if _collisions:
+            raise ValueError(
+                f"Top-level camera names collide with per-arm camera names: {sorted(_collisions)}"
+            )
+        left_arm_cameras = {**config.left_arm_config.cameras, **config.cameras}
+
         left_arm_config = SOFollowerRobotConfig(
             id=f"{config.id}_left" if config.id else None,
             calibration_dir=config.calibration_dir,
@@ -46,7 +59,7 @@ class BiSOFollower(Robot):
             disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
             max_relative_target=config.left_arm_config.max_relative_target,
             use_degrees=config.left_arm_config.use_degrees,
-            cameras=config.left_arm_config.cameras,
+            cameras=left_arm_cameras,
         )
 
         right_arm_config = SOFollowerRobotConfig(
@@ -77,13 +90,12 @@ class BiSOFollower(Robot):
 
     @property
     def _cameras_ft(self) -> dict[str, tuple]:
-        left_arm_cameras_ft = self.left_arm._cameras_ft
+        out: dict[str, tuple] = {}
-        right_arm_cameras_ft = self.right_arm._cameras_ft
+        for k, v in self.left_arm._cameras_ft.items():
-
+            out[k if k in self._top_level_cam_keys else f"left_{k}"] = v
-        return {
+        for k, v in self.right_arm._cameras_ft.items():
-            **{f"left_{k}": v for k, v in left_arm_cameras_ft.items()},
+            out[f"right_{k}"] = v
-            **{f"right_{k}": v for k, v in right_arm_cameras_ft.items()},
+        return out
-        }
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -93,42 +105,21 @@ class BiSOFollower(Robot):
     def action_features(self) -> dict[str, type]:
         return self._motors_ft
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     def setup_motors(self) -> None:
         self.left_arm.setup_motors()
         self.right_arm.setup_motors()
 
     @check_if_not_connected
     def get_observation(self) -> RobotObservation:
-        obs_dict = {}
+        obs_dict: RobotObservation = {}
 
-        # Add "left_" prefix
+        # Add "left_" prefix to per-arm keys; keep top-level camera keys unprefixed.
-        left_obs = self.left_arm.get_observation()
+        for key, value in self.left_arm.get_observation().items():
-        obs_dict.update({f"left_{key}": value for key, value in left_obs.items()})
+            obs_dict[key if key in self._top_level_cam_keys else f"left_{key}"] = value
 
         # Add "right_" prefix
-        right_obs = self.right_arm.get_observation()
+        for key, value in self.right_arm.get_observation().items():
-        obs_dict.update({f"right_{key}": value for key, value in right_obs.items()})
+            obs_dict[f"right_{key}"] = value
 
         return obs_dict
 
@@ -151,8 +142,3 @@ class BiSOFollower(Robot):
         prefixed_sent_action_right = {f"right_{key}": value for key, value in sent_action_right.items()}
 
         return {**prefixed_sent_action_left, **prefixed_sent_action_right}
-
-    @check_if_not_connected
-    def disconnect(self):
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/robots/bi_so_follower/config_bi_so_follower.py

@@ -14,7 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
+
+from lerobot.cameras import CameraConfig
 
 from ..config import RobotConfig
 from ..so_follower import SOFollowerConfig
@@ -27,3 +29,8 @@ class BiSOFollowerConfig(RobotConfig):
 
     left_arm_config: SOFollowerConfig
     right_arm_config: SOFollowerConfig
+
+    # Top-level cameras not attached to a specific side. Keys are kept as-is in
+    # observations (no `left_`/`right_` prefix). Per-arm cameras (declared on
+    # `{left,right}_arm_config.cameras`) are prefixed.
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)

src/lerobot/robots/so_follower/so_follower.py

@@ -68,9 +68,12 @@ class SOFollower(Robot):
 
     @property
     def _cameras_ft(self) -> dict[str, tuple]:
-        return {
+        features: dict[str, tuple] = {}
-            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
+        for cam in self.cameras:
-        }
+            features[cam] = (self.cameras[cam].height, self.cameras[cam].width, 3)
+            if getattr(self.cameras[cam], "use_depth", False):
+                features[f"{cam}_depth"] = (self.cameras[cam].height, self.cameras[cam].width, 1)
+        return features
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -190,6 +193,12 @@ class SOFollower(Robot):
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 
+            if getattr(cam, "use_depth", False):
+                start = time.perf_counter()
+                obs_dict[f"{cam_key}_depth"] = cam.read_latest_depth()
+                dt_ms = (time.perf_counter() - start) * 1e3
+                logger.debug(f"{self} read {cam_key} depth: {dt_ms:.1f}ms")
+
         return obs_dict
 
     @check_if_not_connected

src/lerobot/rollout/context.py

@@ -333,6 +333,7 @@ def build_rollout_context(
                 root=cfg.dataset.root,
                 batch_encoding_size=cfg.dataset.video_encoding_batch_size,
                 camera_encoder=cfg.dataset.camera_encoder,
+                depth_encoder=cfg.dataset.depth_encoder,
                 streaming_encoding=cfg.dataset.streaming_encoding,
                 encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
                 encoder_threads=cfg.dataset.encoder_threads,
@@ -368,6 +369,7 @@ def build_rollout_context(
                 * len(robot.cameras if hasattr(robot, "cameras") else []),
                 batch_encoding_size=cfg.dataset.video_encoding_batch_size,
                 camera_encoder=cfg.dataset.camera_encoder,
+                depth_encoder=cfg.dataset.depth_encoder,
                 streaming_encoding=cfg.dataset.streaming_encoding,
                 encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
                 encoder_threads=cfg.dataset.encoder_threads,

src/lerobot/scripts/lerobot_calibrate.py

@@ -54,6 +54,7 @@ from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     homunculus,

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -133,6 +133,15 @@ Convert image dataset to video format and save locally:
         --new_root /path/to/output/pusht_video \
         --operation.type convert_image_to_video
 
+Convert image dataset (with depth maps) to video format, customizing the depth encoder:
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht_image \
+        --new_root /path/to/output/pusht_video \
+        --operation.type convert_image_to_video \
+        --operation.depth_encoder.depth_min 0.01 \
+        --operation.depth_encoder.depth_max 10.0 \
+        --operation.depth_encoder.use_log true
+
 Convert image dataset to video format and save with new repo_id:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht_image \
@@ -211,6 +220,13 @@ Re-encode videos in-place (overwrites original dataset):
         --operation.camera_encoder.vcodec h264 \
         --operation.overwrite true
 
+Re-encode both RGB and depth videos in a dataset (depth quantization params are preserved):
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht_depth \
+        --operation.type reencode_videos \
+        --operation.camera_encoder.vcodec libx264 \
+        --operation.depth_encoder.vcodec ffv1
+
 Using JSON config file:
     lerobot-edit-dataset \
         --config_path path/to/edit_config.json
@@ -225,7 +241,13 @@ from pathlib import Path
 
 import draccus
 
-from lerobot.configs import VideoEncoderConfig, camera_encoder_defaults, parser
+from lerobot.configs import (
+    DepthEncoderConfig,
+    VideoEncoderConfig,
+    camera_encoder_defaults,
+    depth_encoder_defaults,
+    parser,
+)
 from lerobot.datasets import (
     LeRobotDataset,
     convert_image_to_video_dataset,
@@ -288,6 +310,7 @@ class ModifyTasksConfig(OperationConfig):
 class ConvertImageToVideoConfig(OperationConfig):
     output_dir: str | None = None
     camera_encoder: VideoEncoderConfig = field(default_factory=camera_encoder_defaults)
+    depth_encoder: DepthEncoderConfig = field(default_factory=depth_encoder_defaults)
     episode_indices: list[int] | None = None
     num_workers: int = 4
     max_episodes_per_batch: int | None = None
@@ -309,6 +332,7 @@ class RecomputeStatsConfig(OperationConfig):
 @dataclass
 class ReencodeVideosConfig(OperationConfig):
     camera_encoder: VideoEncoderConfig = field(default_factory=camera_encoder_defaults)
+    depth_encoder: DepthEncoderConfig = field(default_factory=depth_encoder_defaults)
     num_workers: int = 0
     encoder_threads: int | None = None
     overwrite: bool = False
@@ -602,6 +626,7 @@ def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
         output_dir=output_dir,
         repo_id=output_repo_id,
         camera_encoder=getattr(cfg.operation, "camera_encoder", None) or camera_encoder_defaults(),
+        depth_encoder=getattr(cfg.operation, "depth_encoder", None) or depth_encoder_defaults(),
         episode_indices=getattr(cfg.operation, "episode_indices", None),
         num_workers=getattr(cfg.operation, "num_workers", 4),
         max_episodes_per_batch=getattr(cfg.operation, "max_episodes_per_batch", None),
@@ -719,10 +744,14 @@ def handle_reencode_videos(cfg: EditDatasetConfig) -> None:
         shutil.copytree(input_root, output_root)
         dataset = LeRobotDataset(output_repo_id, root=output_root)
 
-    logging.info(f"Re-encoding videos in {output_repo_id} with {cfg.operation.camera_encoder}")
+    logging.info(
+        f"Re-encoding videos in {output_repo_id} with RGB encoder {cfg.operation.camera_encoder} "
+        f"and depth encoder {cfg.operation.depth_encoder}"
+    )
     reencode_dataset(
         dataset,
         camera_encoder=cfg.operation.camera_encoder,
+        depth_encoder=cfg.operation.depth_encoder,
         encoder_threads=cfg.operation.encoder_threads,
         num_workers=cfg.operation.num_workers,
     )

src/lerobot/scripts/lerobot_find_joint_limits.py

@@ -57,6 +57,7 @@ from lerobot.robots import (  # noqa: F401
 from lerobot.teleoperators import (  # noqa: F401
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     gamepad,

src/lerobot/scripts/lerobot_record.py

@@ -137,6 +137,7 @@ from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     homunculus,
@@ -403,6 +404,7 @@ def record(
                 root=cfg.dataset.root,
                 batch_encoding_size=cfg.dataset.video_encoding_batch_size,
                 camera_encoder=cfg.dataset.camera_encoder,
+                depth_encoder=cfg.dataset.depth_encoder,
                 encoder_threads=cfg.dataset.encoder_threads,
                 streaming_encoding=cfg.dataset.streaming_encoding,
                 encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
@@ -432,6 +434,7 @@ def record(
                 image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot.cameras),
                 batch_encoding_size=cfg.dataset.video_encoding_batch_size,
                 camera_encoder=cfg.dataset.camera_encoder,
+                depth_encoder=cfg.dataset.depth_encoder,
                 encoder_threads=cfg.dataset.encoder_threads,
                 streaming_encoding=cfg.dataset.streaming_encoding,
                 encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,

src/lerobot/scripts/lerobot_rollout.py

@@ -174,6 +174,7 @@ from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     homunculus,

src/lerobot/scripts/lerobot_setup_motors.py

@@ -41,6 +41,7 @@ from lerobot.robots import (  # noqa: F401
 )
 from lerobot.teleoperators import (  # noqa: F401
     TeleoperatorConfig,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     koch_leader,

src/lerobot/scripts/lerobot_teleoperate.py

@@ -89,6 +89,7 @@ from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     gamepad,

src/lerobot/teleoperators/bi_openarm_leader/bi_openarm_leader.py

@@ -18,7 +18,8 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..openarm_leader import OpenArmLeader, OpenArmLeaderConfig
 from ..teleoperator import Teleoperator
@@ -27,7 +28,7 @@ from .config_bi_openarm_leader import BiOpenArmLeaderConfig
 logger = logging.getLogger(__name__)
 
 
-class BiOpenArmLeader(Teleoperator):
+class BiOpenArmLeader(BimanualMixin, Teleoperator):
     """
     Bimanual OpenArm Leader Arms
     """
@@ -86,27 +87,6 @@ class BiOpenArmLeader(Teleoperator):
     def feedback_features(self) -> dict[str, type]:
         return {}
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     def setup_motors(self) -> None:
         raise NotImplementedError(
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
@@ -129,8 +109,3 @@ class BiOpenArmLeader(Teleoperator):
     def send_feedback(self, feedback: dict[str, float]) -> None:
         # TODO: Implement force feedback
         raise NotImplementedError
-
-    @check_if_not_connected
-    def disconnect(self) -> None:
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/teleoperators/bi_openarm_leader/config_bi_openarm_leader.py

@@ -23,7 +23,7 @@ from ..openarm_leader import OpenArmLeaderConfigBase
 @TeleoperatorConfig.register_subclass("bi_openarm_leader")
 @dataclass
 class BiOpenArmLeaderConfig(TeleoperatorConfig):
-    """Configuration class for Bi OpenArm Follower robots."""
+    """Configuration class for Bi OpenArm Leader teleoperators."""
 
     left_arm_config: OpenArmLeaderConfigBase
     right_arm_config: OpenArmLeaderConfigBase

src/lerobot/teleoperators/bi_openarm_mini/__init__.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .bi_openarm_mini import BiOpenArmMini
+from .config_bi_openarm_mini import BiOpenArmMiniConfig
+
+__all__ = ["BiOpenArmMini", "BiOpenArmMiniConfig"]

src/lerobot/teleoperators/bi_openarm_mini/bi_openarm_mini.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+
+# 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 logging
+from functools import cached_property
+
+from lerobot.types import RobotAction
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
+
+from ..openarm_mini import OpenArmMini, OpenArmMiniConfig
+from ..teleoperator import Teleoperator
+from .config_bi_openarm_mini import BiOpenArmMiniConfig
+
+logger = logging.getLogger(__name__)
+
+
+class BiOpenArmMini(BimanualMixin, Teleoperator):
+    """Bimanual OpenArm Mini teleoperator.
+
+    Composes two single-arm :class:`OpenArmMini` instances. Action and feedback
+    keys of each arm are namespaced with a ``left_`` / ``right_`` prefix, so a
+    bimanual leader can teleoperate a bimanual OpenArm follower.
+    """
+
+    config_class = BiOpenArmMiniConfig
+    name = "bi_openarm_mini"
+
+    def __init__(self, config: BiOpenArmMiniConfig):
+        super().__init__(config)
+        self.config = config
+
+        # `side` is forced to match left/right regardless of what the user passed
+        # on the per-arm base config — the bimanual wrapper owns the side semantics.
+        left_arm_config = OpenArmMiniConfig(
+            id=f"{config.id}_left" if config.id else None,
+            calibration_dir=config.calibration_dir,
+            port=config.left_arm_config.port,
+            side="left",
+            use_degrees=config.left_arm_config.use_degrees,
+        )
+
+        right_arm_config = OpenArmMiniConfig(
+            id=f"{config.id}_right" if config.id else None,
+            calibration_dir=config.calibration_dir,
+            port=config.right_arm_config.port,
+            side="right",
+            use_degrees=config.right_arm_config.use_degrees,
+        )
+
+        self.left_arm = OpenArmMini(left_arm_config)
+        self.right_arm = OpenArmMini(right_arm_config)
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return {
+            **{f"left_{k}": v for k, v in self.left_arm.action_features.items()},
+            **{f"right_{k}": v for k, v in self.right_arm.action_features.items()},
+        }
+
+    @cached_property
+    def feedback_features(self) -> dict[str, type]:
+        return {
+            **{f"left_{k}": v for k, v in self.left_arm.feedback_features.items()},
+            **{f"right_{k}": v for k, v in self.right_arm.feedback_features.items()},
+        }
+
+    def setup_motors(self) -> None:
+        self.left_arm.setup_motors()
+        self.right_arm.setup_motors()
+
+    @check_if_not_connected
+    def get_action(self) -> RobotAction:
+        action: RobotAction = {}
+        for k, v in self.left_arm.get_action().items():
+            action[f"left_{k}"] = v
+        for k, v in self.right_arm.get_action().items():
+            action[f"right_{k}"] = v
+        return action
+
+    @check_if_not_connected
+    def send_feedback(self, feedback: dict[str, float]) -> None:
+        left_fb = {k.removeprefix("left_"): v for k, v in feedback.items() if k.startswith("left_")}
+        right_fb = {k.removeprefix("right_"): v for k, v in feedback.items() if k.startswith("right_")}
+        if left_fb:
+            self.left_arm.send_feedback(left_fb)
+        if right_fb:
+            self.right_arm.send_feedback(right_fb)

src/lerobot/teleoperators/bi_openarm_mini/config_bi_openarm_mini.py

@@ -0,0 +1,29 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+
+from ..config import TeleoperatorConfig
+from ..openarm_mini import OpenArmMiniConfigBase
+
+
+@TeleoperatorConfig.register_subclass("bi_openarm_mini")
+@dataclass
+class BiOpenArmMiniConfig(TeleoperatorConfig):
+    """Configuration class for Bi OpenArm Mini teleoperators."""
+
+    left_arm_config: OpenArmMiniConfigBase
+    right_arm_config: OpenArmMiniConfigBase

src/lerobot/teleoperators/bi_rebot_102_leader/__init__.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .bi_rebot_102_leader import BiRebotArm102Leader
+from .bi_rebot_102_leader import BiRebot102Leader
-from .config_bi_rebot_102_leader import BiRebotArm102LeaderConfig
+from .config_bi_rebot_102_leader import BiRebot102LeaderConfig
 
-__all__ = ["BiRebotArm102Leader", "BiRebotArm102LeaderConfig"]
+__all__ = ["BiRebot102Leader", "BiRebot102LeaderConfig"]

src/lerobot/teleoperators/bi_rebot_102_leader/bi_rebot_102_leader.py

@@ -18,16 +18,17 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..rebot_102_leader import RebotArm102Leader, RebotArm102LeaderTeleopConfig
 from ..teleoperator import Teleoperator
-from .config_bi_rebot_102_leader import BiRebotArm102LeaderConfig
+from .config_bi_rebot_102_leader import BiRebot102LeaderConfig
 
 logger = logging.getLogger(__name__)
 
 
-class BiRebotArm102Leader(Teleoperator):
+class BiRebot102Leader(BimanualMixin, Teleoperator):
     """Bimanual Seeed Studio StarArm102 / reBot Arm 102 leader.
 
     Composes two single-arm :class:`RebotArm102Leader` instances. Action keys of
@@ -35,10 +36,10 @@ class BiRebotArm102Leader(Teleoperator):
     leader can teleoperate a bimanual reBot B601 follower.
     """
 
-    config_class = BiRebotArm102LeaderConfig
+    config_class = BiRebot102LeaderConfig
     name = "bi_rebot_102_leader"
 
-    def __init__(self, config: BiRebotArm102LeaderConfig):
+    def __init__(self, config: BiRebot102LeaderConfig):
         super().__init__(config)
         self.config = config
 
@@ -76,27 +77,6 @@ class BiRebotArm102Leader(Teleoperator):
     def feedback_features(self) -> dict[str, type]:
         return {}
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     @check_if_not_connected
     def get_action(self) -> RobotAction:
         action_dict = {}
@@ -106,8 +86,3 @@ class BiRebotArm102Leader(Teleoperator):
 
     def send_feedback(self, feedback: dict[str, float]) -> None:
         raise NotImplementedError("Feedback is not implemented for the reBot Arm 102 leader.")
-
-    @check_if_not_connected
-    def disconnect(self) -> None:
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/teleoperators/bi_rebot_102_leader/config_bi_rebot_102_leader.py

@@ -22,7 +22,7 @@ from ..rebot_102_leader import RebotArm102LeaderConfig
 
 @TeleoperatorConfig.register_subclass("bi_rebot_102_leader")
 @dataclass
-class BiRebotArm102LeaderConfig(TeleoperatorConfig):
+class BiRebot102LeaderConfig(TeleoperatorConfig):
     """Configuration class for the bimanual reBot Arm 102 leader teleoperator."""
 
     left_arm_config: RebotArm102LeaderConfig

src/lerobot/teleoperators/bi_so_leader/bi_so_leader.py

@@ -17,7 +17,9 @@
 import logging
 from functools import cached_property
 
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.types import RobotAction
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..so_leader import SOLeader, SOLeaderTeleopConfig
 from ..teleoperator import Teleoperator
@@ -26,7 +28,7 @@ from .config_bi_so_leader import BiSOLeaderConfig
 logger = logging.getLogger(__name__)
 
 
-class BiSOLeader(Teleoperator):
+class BiSOLeader(BimanualMixin, Teleoperator):
     """
     [Bimanual SO Leader Arms](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio
     """
@@ -67,33 +69,12 @@ class BiSOLeader(Teleoperator):
     def feedback_features(self) -> dict[str, type]:
         return {}
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     def setup_motors(self) -> None:
         self.left_arm.setup_motors()
         self.right_arm.setup_motors()
 
     @check_if_not_connected
-    def get_action(self) -> dict[str, float]:
+    def get_action(self) -> RobotAction:
         action_dict = {}
 
         # Add "left_" prefix
@@ -109,8 +90,3 @@ class BiSOLeader(Teleoperator):
     def send_feedback(self, feedback: dict[str, float]) -> None:
         # TODO: Implement force feedback
         raise NotImplementedError
-
-    @check_if_not_connected
-    def disconnect(self) -> None:
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/teleoperators/openarm_mini/__init__.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# 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.
@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .config_openarm_mini import OpenArmMiniConfig
+from .config_openarm_mini import OpenArmMiniConfig, OpenArmMiniConfigBase
 from .openarm_mini import OpenArmMini
 
-__all__ = ["OpenArmMini", "OpenArmMiniConfig"]
+__all__ = ["OpenArmMini", "OpenArmMiniConfig", "OpenArmMiniConfigBase"]

src/lerobot/teleoperators/openarm_mini/config_openarm_mini.py

@@ -19,12 +19,21 @@ from dataclasses import dataclass
 from ..config import TeleoperatorConfig
 
 
-@TeleoperatorConfig.register_subclass("openarm_mini")
 @dataclass
-class OpenArmMiniConfig(TeleoperatorConfig):
+class OpenArmMiniConfigBase:
-    """Configuration for OpenArm Mini teleoperator with Feetech motors (dual arms)."""
+    """Base configuration for the OpenArm Mini teleoperator (Feetech STS3215, 7DOF + gripper)."""
 
-    port_right: str = "/dev/ttyUSB0"
+    # Serial port for the Feetech bus (e.g., "/dev/ttyUSB0").
-    port_left: str = "/dev/ttyUSB1"
+    port: str
+
+    # Side of the arm: "left" or "right". Controls per-joint direction flips applied
+    # during readout. If `None`, no flipping is applied.
+    side: str | None = None
 
     use_degrees: bool = True
+
+
+@TeleoperatorConfig.register_subclass("openarm_mini")
+@dataclass
+class OpenArmMiniConfig(TeleoperatorConfig, OpenArmMiniConfigBase):
+    pass

src/lerobot/teleoperators/openarm_mini/openarm_mini.py

@@ -31,22 +31,22 @@ from .config_openarm_mini import OpenArmMiniConfig
 
 logger = logging.getLogger(__name__)
 
-# Motors whose direction is inverted during readout
+# Per-side motor direction flips applied during readout.
-RIGHT_MOTORS_TO_FLIP = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"]
+SIDE_MOTORS_TO_FLIP: dict[str, list[str]] = {
-LEFT_MOTORS_TO_FLIP = ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"]
+    "left": ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"],
+    "right": ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"],
+}
 
-# Leader joint 6 maps to follower joint 7 and vice versa
+# Leader joint 6 ↔ follower joint 7 (symmetric — its own inverse).
 JOINT_REMAP = {"joint_6": "joint_7", "joint_7": "joint_6"}
-JOINT_REMAP_REVERSE = {"joint_7": "joint_6", "joint_6": "joint_7"}
 
 GRIPPER_TELEOP_TO_DEGREES = -0.65
 
 
 class OpenArmMini(Teleoperator):
-    """
+    """OpenArm Mini single-arm teleoperator (Feetech STS3215, 7DOF + gripper).
-    OpenArm Mini Teleoperator with dual Feetech-based arms (8 motors per arm).
 
-    Each arm has 7 joints plus a gripper, using Feetech STS3215 servos.
+    For the bimanual setup, see :class:`BiOpenArmMini` which composes two of these.
     """
 
     config_class = OpenArmMiniConfig
@@ -56,9 +56,12 @@ class OpenArmMini(Teleoperator):
         super().__init__(config)
         self.config = config
 
+        if config.side is not None and config.side not in SIDE_MOTORS_TO_FLIP:
+            raise ValueError(f"Invalid side '{config.side}'; expected 'left', 'right', or None.")
+        self._motors_to_flip: list[str] = SIDE_MOTORS_TO_FLIP.get(config.side, []) if config.side else []
+
         norm_mode_body = MotorNormMode.DEGREES
-
+        motors = {
-        motors_right = {
             "joint_1": Motor(1, "sts3215", norm_mode_body),
             "joint_2": Motor(2, "sts3215", norm_mode_body),
             "joint_3": Motor(3, "sts3215", norm_mode_body),
@@ -69,46 +72,15 @@ class OpenArmMini(Teleoperator):
             "gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
         }
 
-        motors_left = {
+        self.bus = FeetechMotorsBus(
-            "joint_1": Motor(1, "sts3215", norm_mode_body),
+            port=self.config.port,
-            "joint_2": Motor(2, "sts3215", norm_mode_body),
+            motors=motors,
-            "joint_3": Motor(3, "sts3215", norm_mode_body),
+            calibration=self.calibration,
-            "joint_4": Motor(4, "sts3215", norm_mode_body),
-            "joint_5": Motor(5, "sts3215", norm_mode_body),
-            "joint_6": Motor(6, "sts3215", norm_mode_body),
-            "joint_7": Motor(7, "sts3215", norm_mode_body),
-            "gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
-        }
-
-        cal_right = {
-            k.replace("right_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("right_")
-        }
-        cal_left = {
-            k.replace("left_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("left_")
-        }
-
-        self.bus_right = FeetechMotorsBus(
-            port=self.config.port_right,
-            motors=motors_right,
-            calibration=cal_right,
-        )
-
-        self.bus_left = FeetechMotorsBus(
-            port=self.config.port_left,
-            motors=motors_left,
-            calibration=cal_left,
         )
 
     @property
     def action_features(self) -> dict[str, type]:
-        # Right first, then left — matches the robot (BiOpenArmFollower) ordering
+        return {f"{motor}.pos": float for motor in self.bus.motors}
-        # and the dataset feature names recorded during data collection.
-        features: dict[str, type] = {}
-        for motor in self.bus_right.motors:
-            features[f"right_{motor}.pos"] = float
-        for motor in self.bus_left.motors:
-            features[f"left_{motor}.pos"] = float
-        return features
 
     @property
     def feedback_features(self) -> dict[str, type]:
@@ -116,14 +88,12 @@ class OpenArmMini(Teleoperator):
 
     @property
     def is_connected(self) -> bool:
-        return self.bus_right.is_connected and self.bus_left.is_connected
+        return self.bus.is_connected
 
     @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
-        logger.info(f"Connecting right arm on {self.config.port_right}...")
+        logger.info(f"Connecting arm on {self.config.port}...")
-        self.bus_right.connect()
+        self.bus.connect()
-        logger.info(f"Connecting left arm on {self.config.port_left}...")
-        self.bus_left.connect()
 
         if calibrate:
             self.calibrate()
@@ -133,14 +103,14 @@ class OpenArmMini(Teleoperator):
 
     @property
     def is_calibrated(self) -> bool:
-        return self.bus_right.is_calibrated and self.bus_left.is_calibrated
+        return self.bus.is_calibrated
 
     def calibrate(self) -> None:
         """
-        Run calibration procedure for OpenArm Mini.
+        Run calibration procedure for a single OpenArm Mini arm.
 
         1. Disable torque
-        2. Ask user to position arms in hanging position with grippers closed
+        2. Ask user to position arm in hanging position with gripper closed
         3. Set this as zero position via half-turn homing
         4. Interactive gripper calibration (open/close positions)
         5. Save calibration
@@ -152,70 +122,51 @@ class OpenArmMini(Teleoperator):
             )
             if user_input.strip().lower() != "c":
                 logger.info(f"Using existing calibration for {self.id}")
-                cal_right = {
+                self.bus.write_calibration(self.calibration)
-                    k.replace("right_", ""): v for k, v in self.calibration.items() if k.startswith("right_")
-                }
-                cal_left = {
-                    k.replace("left_", ""): v for k, v in self.calibration.items() if k.startswith("left_")
-                }
-                self.bus_right.write_calibration(cal_right)
-                self.bus_left.write_calibration(cal_left)
                 return
 
         logger.info(f"\nRunning calibration for {self}")
 
-        self._calibrate_arm("right", self.bus_right)
+        self.bus.disable_torque()
-        self._calibrate_arm("left", self.bus_left)
 
-        self._save_calibration()
+        logger.info("Setting Phase to 12 for all motors...")
-        print(f"\nCalibration complete and saved to {self.calibration_fpath}")
+        for motor in self.bus.motors:
+            self.bus.write("Phase", motor, 12)
 
-    def _calibrate_arm(self, arm_name: str, bus: FeetechMotorsBus) -> None:
+        for motor in self.bus.motors:
-        """Calibrate a single arm with Feetech motors."""
+            self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
-        logger.info(f"\n=== Calibrating {arm_name.upper()} arm ===")
-
-        bus.disable_torque()
-
-        logger.info(f"Setting Phase to 12 for all motors in {arm_name.upper()} arm...")
-        for motor in bus.motors:
-            bus.write("Phase", motor, 12)
-
-        for motor in bus.motors:
-            bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
 
         input(
-            f"\nCalibration: Zero Position ({arm_name.upper()} arm)\n"
+            "\nCalibration: Zero Position\n"
             "Position the arm in the following configuration:\n"
             "  - Arm hanging straight down\n"
             "  - Gripper closed\n"
             "Press ENTER when ready..."
         )
 
-        homing_offsets = bus.set_half_turn_homings()
+        homing_offsets = self.bus.set_half_turn_homings()
-        logger.info(f"{arm_name.capitalize()} arm zero position set.")
+        logger.info("Arm zero position set.")
 
-        print(f"\nSetting motor ranges for {arm_name.upper()} arm\n")
+        print("\nSetting motor ranges\n")
 
         if self.calibration is None:
             self.calibration = {}
 
-        motor_resolution = bus.model_resolution_table[list(bus.motors.values())[0].model]
+        motor_resolution = self.bus.model_resolution_table[list(self.bus.motors.values())[0].model]
         max_res = motor_resolution - 1
 
-        for motor_name, motor in bus.motors.items():
+        for motor_name, motor in self.bus.motors.items():
-            prefixed_name = f"{arm_name}_{motor_name}"
-
             if motor_name == "gripper":
                 input(
-                    f"\nGripper Calibration ({arm_name.upper()} arm)\n"
+                    "\nGripper Calibration\n"
-                    f"Step 1: CLOSE the gripper fully\n"
+                    "Step 1: CLOSE the gripper fully\n"
-                    f"Press ENTER when gripper is closed..."
+                    "Press ENTER when gripper is closed..."
                 )
-                closed_pos = bus.read("Present_Position", motor_name, normalize=False)
+                closed_pos = self.bus.read("Present_Position", motor_name, normalize=False)
                 logger.info(f"  Gripper closed position recorded: {closed_pos}")
 
                 input("\nStep 2: OPEN the gripper fully\nPress ENTER when gripper is fully open...")
-                open_pos = bus.read("Present_Position", motor_name, normalize=False)
+                open_pos = self.bus.read("Present_Position", motor_name, normalize=False)
                 logger.info(f"  Gripper open position recorded: {open_pos}")
 
                 if closed_pos < open_pos:
@@ -228,16 +179,16 @@ class OpenArmMini(Teleoperator):
                     drive_mode = 1
 
                 logger.info(
-                    f"  {prefixed_name}: range set to [{range_min}, {range_max}] "
+                    f"  {motor_name}: range set to [{range_min}, {range_max}] "
                     f"(0=closed, 100=open, drive_mode={drive_mode})"
                 )
             else:
                 range_min = 0
                 range_max = max_res
                 drive_mode = 0
-                logger.info(f"  {prefixed_name}: range set to [0, {max_res}] (full motor range)")
+                logger.info(f"  {motor_name}: range set to [0, {max_res}] (full motor range)")
 
-            self.calibration[prefixed_name] = MotorCalibration(
+            self.calibration[motor_name] = MotorCalibration(
                 id=motor.id,
                 drive_mode=drive_mode,
                 homing_offset=homing_offsets[motor_name],
@@ -245,108 +196,68 @@ class OpenArmMini(Teleoperator):
                 range_max=range_max,
             )
 
-        cal_for_bus = {
+        self.bus.write_calibration(self.calibration)
-            k.replace(f"{arm_name}_", ""): v
+        self._save_calibration()
-            for k, v in self.calibration.items()
+        print(f"\nCalibration complete and saved to {self.calibration_fpath}")
-            if k.startswith(f"{arm_name}_")
-        }
-        bus.write_calibration(cal_for_bus)
 
     def configure(self) -> None:
-        self.bus_right.disable_torque()
+        self.bus.disable_torque()
-        self.bus_right.configure_motors()
+        self.bus.configure_motors()
-        for motor in self.bus_right.motors:
+        for motor in self.bus.motors:
-            self.bus_right.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+            self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
-
-        self.bus_left.disable_torque()
-        self.bus_left.configure_motors()
-        for motor in self.bus_left.motors:
-            self.bus_left.write("Operating_Mode", motor, OperatingMode.POSITION.value)
 
     def setup_motors(self) -> None:
-        print("\nSetting up RIGHT arm motors...")
+        for motor in reversed(self.bus.motors):
-        for motor in reversed(self.bus_right.motors):
+            input(f"Connect the controller board to the '{motor}' motor only and press enter.")
-            input(f"Connect the controller board to the RIGHT '{motor}' motor only and press enter.")
+            self.bus.setup_motor(motor)
-            self.bus_right.setup_motor(motor)
+            print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
-            print(f"RIGHT '{motor}' motor id set to {self.bus_right.motors[motor].id}")
-
-        print("\nSetting up LEFT arm motors...")
-        for motor in reversed(self.bus_left.motors):
-            input(f"Connect the controller board to the LEFT '{motor}' motor only and press enter.")
-            self.bus_left.setup_motor(motor)
-            print(f"LEFT '{motor}' motor id set to {self.bus_left.motors[motor].id}")
 
     @check_if_not_connected
     def get_action(self) -> RobotAction:
-        """Get current action from both arms (read positions from all motors)."""
+        """Get current action (read positions from all motors)."""
         start = time.perf_counter()
 
-        right_positions = self.bus_right.sync_read("Present_Position")
+        positions = self.bus.sync_read("Present_Position")
-        left_positions = self.bus_left.sync_read("Present_Position")
 
-        # Right first, then left — matches the robot (BiOpenArmFollower) ordering
-        # and the dataset feature names recorded during data collection.
         # Joint 6↔7 remap: leader joint_6 → follower joint_7 and vice versa.
+        # Per-side direction flip is applied based on the configured `side`.
         action: dict[str, Any] = {}
-        for motor, val in right_positions.items():
+        for motor, val in positions.items():
             target = JOINT_REMAP.get(motor, motor)
             if motor == "gripper":
                 # Convert gripper from teleop 0-100 to openarms degrees: 0→0°, 100→-65°
-                action[f"right_{target}.pos"] = val * GRIPPER_TELEOP_TO_DEGREES
+                action[f"{target}.pos"] = val * GRIPPER_TELEOP_TO_DEGREES
             else:
-                action[f"right_{target}.pos"] = -val if motor in RIGHT_MOTORS_TO_FLIP else val
+                action[f"{target}.pos"] = -val if motor in self._motors_to_flip else val
-        for motor, val in left_positions.items():
-            target = JOINT_REMAP.get(motor, motor)
-            if motor == "gripper":
-                action[f"left_{target}.pos"] = val * GRIPPER_TELEOP_TO_DEGREES
-            else:
-                action[f"left_{target}.pos"] = -val if motor in LEFT_MOTORS_TO_FLIP else val
 
         dt_ms = (time.perf_counter() - start) * 1e3
         logger.debug(f"{self} read action: {dt_ms:.1f}ms")
         return action
 
     def enable_torque(self) -> None:
-        """Enable torque on both arms for position control."""
+        self.bus.enable_torque()
-        self.bus_right.enable_torque()
-        self.bus_left.enable_torque()
 
     def disable_torque(self) -> None:
-        """Disable torque on both arms for free movement."""
+        self.bus.disable_torque()
-        self.bus_right.disable_torque()
-        self.bus_left.disable_torque()
 
     def write_goal_positions(self, positions: dict[str, float]) -> None:
         """Write goal positions to motors (inverse of get_action flip/gripper/remap logic)."""
-        right_goals: dict[str, float] = {}
+        goals: dict[str, float] = {}
-        left_goals: dict[str, float] = {}
-
         for key, val in positions.items():
             if not key.endswith(".pos"):
                 continue
-            motor_name = key.removesuffix(".pos")
+            base = key.removesuffix(".pos")
-            if motor_name.startswith("right_"):
+            # JOINT_REMAP is symmetric (its own inverse).
-                base = motor_name.removeprefix("right_")
+            target = JOINT_REMAP.get(base, base)
-                # Reverse remap: follower joint_7 → leader joint_6 and vice versa
+            if base == "gripper":
-                target = JOINT_REMAP_REVERSE.get(base, base)
+                # Convert robot degrees to teleop 0-100: 0°→0, -65°→100
-                if base == "gripper":
+                goals[target] = val / GRIPPER_TELEOP_TO_DEGREES
-                    # Convert robot degrees to teleop 0-100: 0°→0, -65°→100
+            else:
-                    right_goals[target] = val / GRIPPER_TELEOP_TO_DEGREES
+                # Un-flip using the ORIGINAL motor name (target = leader motor)
-                else:
+                goals[target] = -val if target in self._motors_to_flip else val
-                    # Un-flip using the ORIGINAL motor name (target = leader motor)
-                    right_goals[target] = -val if target in RIGHT_MOTORS_TO_FLIP else val
-            elif motor_name.startswith("left_"):
-                base = motor_name.removeprefix("left_")
-                target = JOINT_REMAP_REVERSE.get(base, base)
-                if base == "gripper":
-                    left_goals[target] = val / GRIPPER_TELEOP_TO_DEGREES
-                else:
-                    left_goals[target] = -val if target in LEFT_MOTORS_TO_FLIP else val
 
-        if right_goals:
+        if goals:
-            self.bus_right.sync_write("Goal_Position", right_goals)
+            self.bus.sync_write("Goal_Position", goals)
-        if left_goals:
-            self.bus_left.sync_write("Goal_Position", left_goals)
 
     @check_if_not_connected
     def send_feedback(self, feedback: dict[str, float]) -> None:
@@ -354,6 +265,5 @@ class OpenArmMini(Teleoperator):
 
     @check_if_not_connected
     def disconnect(self) -> None:
-        self.bus_right.disconnect()
+        self.bus.disconnect()
-        self.bus_left.disconnect()
         logger.info(f"{self} disconnected.")

src/lerobot/teleoperators/utils.py

@@ -99,14 +99,18 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> "Teleoperator":
         from .openarm_mini import OpenArmMini
 
         return OpenArmMini(config)
+    elif config.type == "bi_openarm_mini":
+        from .bi_openarm_mini import BiOpenArmMini
+
+        return BiOpenArmMini(config)
     elif config.type == "rebot_102_leader":
         from .rebot_102_leader import RebotArm102Leader
 
         return RebotArm102Leader(config)
     elif config.type == "bi_rebot_102_leader":
-        from .bi_rebot_102_leader import BiRebotArm102Leader
+        from .bi_rebot_102_leader import BiRebot102Leader
 
-        return BiRebotArm102Leader(config)
+        return BiRebot102Leader(config)
     else:
         try:
             return cast("Teleoperator", make_device_from_device_class(config))

src/lerobot/utils/bimanual.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any
+
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+
+
+class BimanualMixin:
+    """Lifecycle delegation for bimanual robots and teleoperators.
+
+    Concrete subclasses must populate ``self.left_arm`` and ``self.right_arm`` in
+    their own ``__init__``. They retain ownership of feature dicts and the
+    data-routing methods (``get_action`` / ``send_action`` / ``get_observation`` /
+    ``send_feedback``), which vary per-embodiment.
+
+    Inherit before the ``Robot`` / ``Teleoperator`` base so the mixin's methods
+    take precedence in the MRO::
+
+        class BiFooFollower(BimanualMixin, Robot): ...
+    """
+
+    left_arm: Any
+    right_arm: Any
+
+    @property
+    def is_connected(self) -> bool:
+        return self.left_arm.is_connected and self.right_arm.is_connected
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
+
+    @check_if_already_connected
+    def connect(self, calibrate: bool = True) -> None:
+        self.left_arm.connect(calibrate)
+        self.right_arm.connect(calibrate)
+
+    def calibrate(self) -> None:
+        self.left_arm.calibrate()
+        self.right_arm.calibrate()
+
+    def configure(self) -> None:
+        self.left_arm.configure()
+        self.right_arm.configure()
+
+    @check_if_not_connected
+    def disconnect(self) -> None:
+        self.left_arm.disconnect()
+        self.right_arm.disconnect()

src/lerobot/utils/feature_utils.py

@@ -51,7 +51,9 @@ def hw_to_dataset_features(
 
     This function takes a dictionary describing hardware outputs (like joint states
     or camera image shapes) and formats it into the standard LeRobot feature
-    specification.
+    specification. Single-channel cameras (shape ``(H, W, 1)``) are flagged as depth
+    maps via ``info["is_depth_map"] = True``; three-channel cameras ``(H, W, 3)`` are
+    treated as RGB.
 
     Args:
         hw_features (dict): Dictionary mapping feature names to their type (float for
@@ -61,7 +63,7 @@ def hw_to_dataset_features(
         use_video (bool): If True, image features are marked as "video", otherwise "image".
 
     Returns:
-        dict: A LeRobot features dictionary.
+        dict: A LeRobot features dictionary. Depth cameras carry ``info["is_depth_map"] = True``.
     """
     features = {}
     joint_fts = {
@@ -69,6 +71,7 @@ def hw_to_dataset_features(
         for key, ftype in hw_features.items()
         if ftype is float or (isinstance(ftype, PolicyFeature) and ftype.type != FeatureType.VISUAL)
     }
+    # TODO(CarolinePascal): we should not rely on the shape to determine if a feature is a camera !
     cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)}
 
     if joint_fts and prefix == ACTION:
@@ -86,11 +89,19 @@ def hw_to_dataset_features(
         }
 
     for key, shape in cam_fts.items():
-        features[f"{prefix}.images.{key}"] = {
+        dtype = "video" if use_video else "image"
-            "dtype": "video" if use_video else "image",
+        if len(shape) == 3 and shape[2] in (1, 3):
-            "shape": shape,
+            features[f"{prefix}.images.{key}"] = {
-            "names": ["height", "width", "channels"],
+                "dtype": dtype,
-        }
+                "shape": shape,
+                "names": ["height", "width", "channels"],
+                "info": {"is_depth_map": shape[2] == 1},
+            }
+        else:
+            raise ValueError(
+                f"Camera feature '{key}' has shape {shape}. "
+                f"Expected a 3-tuple (H, W, C), e.g. (480, 640, 3) for RGB or (480, 640, 1) for depth."
+            )
 
     _validate_feature_names(features)
     return features
@@ -149,11 +160,11 @@ def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFea
             type = FeatureType.VISUAL
             if len(shape) != 3:
                 raise ValueError(f"Number of dimensions of {key} != 3 (shape={shape})")
-
+            else:
-            names = ft["names"]
+                names = ft["names"]
-            # Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets.
+                # Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets.
-            if names[2] in ["channel", "channels"]:  # (h, w, c) -> (c, h, w)
+                if names[2] in ["channel", "channels"]:  # (h, w, c) -> (c, h, w)
-                shape = (shape[2], shape[0], shape[1])
+                    shape = (shape[2], shape[0], shape[1])
         elif key == OBS_ENV_STATE:
             type = FeatureType.ENV
         elif key.startswith(OBS_STR):

src/lerobot/utils/visualization_utils.py

@@ -107,7 +107,14 @@ def log_rerun_data(
                     for i, vi in enumerate(arr):
                         rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
                 else:
-                    img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
+                    if arr.shape[-1] == 1:
+                        img_entity = (
+                            rr.DepthImage(arr, colormap=rr.components.Colormap.Viridis).compress()
+                            if compress_images
+                            else rr.DepthImage(arr, colormap=rr.components.Colormap.Viridis)
+                        )
+                    else:
+                        img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
                     rr.log(key, entity=img_entity, static=True)
 
     if action:

tests/annotations/test_frames.py

@@ -208,14 +208,14 @@ def test_episode_clip_path_trims_via_reencode_video(tmp_path: Path, monkeypatch)
     def fake_reencode(
         input_video_path,
         output_video_path,
-        camera_encoder=None,
+        video_encoder=None,
         overwrite=False,
         start_time_s=None,
         end_time_s=None,
     ):
         captured.update(
             src=Path(input_video_path),
-            encoder=camera_encoder,
+            encoder=video_encoder,
             start_time_s=start_time_s,
             end_time_s=end_time_s,
         )

tests/annotations/test_writer.py

@@ -28,6 +28,7 @@ import pytest
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 pytest.importorskip("pandas", reason="pandas is required (install lerobot[dataset])")
 
+import pandas as pd  # noqa: E402
 import pyarrow.parquet as pq  # noqa: E402
 
 from lerobot.annotations.steerable_pipeline.reader import iter_episodes  # noqa: E402
@@ -344,6 +345,78 @@ def test_annotation_metadata_sync_allows_non_streaming_load(
     assert len(dataset) == 24
 
 
+def _build_packed_dataset(root: Path, episode_lengths: list[int], *, fps: int = 10) -> Path:
+    """Pack several episodes into a single shard (vs build_annotation_dataset's one-per-file),
+    so the writer's rewrite must re-emit one row group per episode instead of collapsing them."""
+    from lerobot.datasets.io_utils import write_tasks
+    from lerobot.utils.io_utils import write_json
+
+    data_dir = root / "data" / "chunk-000"
+    data_dir.mkdir(parents=True, exist_ok=True)
+
+    episode_index, frame_index, timestamp, task_index, subtask_index = [], [], [], [], []
+    for ep, length in enumerate(episode_lengths):
+        episode_index += [ep] * length
+        frame_index += list(range(length))
+        timestamp += [round(i / fps, 6) for i in range(length)]
+        task_index += [0] * length
+        subtask_index += [0] * length  # legacy column the writer must drop
+    pd.DataFrame(
+        {
+            "episode_index": episode_index,
+            "frame_index": frame_index,
+            "timestamp": timestamp,
+            "task_index": task_index,
+            "subtask_index": subtask_index,
+        }
+    ).to_parquet(data_dir / "file-000.parquet", index=False)
+
+    tasks_df = pd.DataFrame({"task_index": [0]}, index=pd.Index(["do the thing"], name="task"))
+    write_tasks(tasks_df, root)
+    write_json(
+        {"codebase_version": "v3.1", "fps": fps, "features": {}, "total_episodes": len(episode_lengths)},
+        root / "meta" / "info.json",
+    )
+    return root
+
+
+def test_writer_one_row_group_per_episode(tmp_path: Path) -> None:
+    """Rewriting a packed shard must keep one row group per episode, not collapse
+    every episode into a single giant row group."""
+    episode_lengths = [4, 6, 5]  # unequal lengths, all in one shard
+    root = _build_packed_dataset(tmp_path / "ds", episode_lengths)
+    shard = root / "data" / "chunk-000" / "file-000.parquet"
+    assert pq.ParquetFile(shard).metadata.num_row_groups == 1, "fixture should start collapsed"
+
+    staging_dir = tmp_path / "stage"
+    for ep in range(len(episode_lengths)):
+        _stage_episode(
+            staging_dir,
+            ep,
+            plan=[
+                {
+                    "role": "assistant",
+                    "content": f"subtask for ep {ep}",
+                    "style": "subtask",
+                    "timestamp": 0.0,
+                    "tool_calls": None,
+                }
+            ],
+        )
+
+    records = list(iter_episodes(root))
+    LanguageColumnsWriter().write_all(records, staging_dir, root)
+
+    # One row group per episode, with row counts matching the episode lengths.
+    md = pq.ParquetFile(shard).metadata
+    assert md.num_row_groups == len(episode_lengths)
+    assert [md.row_group(i).num_rows for i in range(md.num_row_groups)] == episode_lengths
+    # Language columns are still present after the per-episode rewrite.
+    table = pq.read_table(shard)
+    assert "language_persistent" in table.column_names
+    assert "language_events" in table.column_names
+
+
 def test_speech_atom_shape_matches_plan_spec() -> None:
     atom = speech_atom(2.5, "I'm cleaning up!")
     assert atom["role"] == "assistant"

tests/datasets/test_aggregate.py

@@ -29,7 +29,30 @@ from lerobot.configs import VIDEO_ENCODER_INFO_KEYS
 from lerobot.datasets.aggregate import aggregate_datasets
 from lerobot.datasets.feature_utils import features_equal_for_merge
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from tests.fixtures.constants import DUMMY_REPO_ID
+from tests.fixtures.constants import (
+    DUMMY_CAMERA_FEATURES_WITH_DEPTH,
+    DUMMY_REPO_ID,
+)
+
+
+def assert_data_shards_one_row_group_per_episode(root):
+    """Every aggregated DATA shard must have exactly one parquet row group per episode."""
+    import pyarrow.parquet as pq
+
+    shards = sorted((root / "data").rglob("*.parquet"))
+    assert shards, f"no data shards found under {root}/data"
+    n_episodes = 0
+    for shard in shards:
+        pf = pq.ParquetFile(shard)
+        episodes = pf.read(columns=["episode_index"]).column("episode_index").to_pylist()
+        assert pf.metadata.num_row_groups == len(set(episodes)), shard
+        for i in range(pf.metadata.num_row_groups):
+            rg_episodes = set(
+                pf.read_row_group(i, columns=["episode_index"]).column("episode_index").to_pylist()
+            )
+            assert len(rg_episodes) == 1, f"{shard} row group {i} spans episodes {rg_episodes}"
+        n_episodes += len(set(episodes))
+    return n_episodes
 
 
 def assert_episode_and_frame_counts(aggr_ds, expected_episodes, expected_frames):
@@ -191,6 +214,26 @@ def assert_dataset_iteration_works(aggr_ds):
         pass
 
 
+def assert_depth_keys_preserved(aggr_ds, ds_0, ds_1):
+    """Test that depth keys are correctly preserved after aggregation.
+
+    Ensures that the ``is_depth_map`` marker on visual features survives
+    aggregation, so that downstream consumers (e.g. the dataset reader's
+    depth decoding path) keep working on the merged dataset.
+    """
+    expected_depth_keys = set(ds_0.meta.depth_keys)
+    assert expected_depth_keys == set(ds_1.meta.depth_keys), (
+        "Source datasets disagree on depth_keys; test setup is inconsistent"
+    )
+    actual_depth_keys = set(aggr_ds.meta.depth_keys)
+    assert actual_depth_keys == expected_depth_keys, (
+        f"Expected depth_keys {expected_depth_keys}, got {actual_depth_keys}"
+    )
+    for key in expected_depth_keys:
+        info = aggr_ds.meta.info.features[key].get("info") or {}
+        assert info.get("is_depth_map") is True, f"Depth marker lost on feature {key!r} after aggregation"
+
+
 def assert_video_timestamps_within_bounds(aggr_ds):
     """Test that all video timestamps are within valid bounds for their respective video files.
 
@@ -240,7 +283,11 @@ def assert_video_timestamps_within_bounds(aggr_ds):
 
 
 def test_aggregate_datasets(tmp_path, lerobot_dataset_factory):
-    """Test basic aggregation functionality with standard parameters."""
+    """Test basic aggregation functionality with standard parameters.
+
+    Source datasets include both RGB and depth video features so the same
+    aggregation flow is exercised on the ``is_depth_map`` branch.
+    """
     ds_0_num_frames = 400
     ds_1_num_frames = 800
     ds_0_num_episodes = 10
@@ -252,14 +299,21 @@ def test_aggregate_datasets(tmp_path, lerobot_dataset_factory):
         repo_id=f"{DUMMY_REPO_ID}_0",
         total_episodes=ds_0_num_episodes,
         total_frames=ds_0_num_frames,
+        camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
     )
     ds_1 = lerobot_dataset_factory(
         root=tmp_path / "test_1",
         repo_id=f"{DUMMY_REPO_ID}_1",
         total_episodes=ds_1_num_episodes,
         total_frames=ds_1_num_frames,
+        camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
     )
 
+    # Confirm depth was actually wired into the source datasets so the
+    # rest of the assertions exercise the depth aggregation path.
+    assert len(ds_0.meta.depth_keys) > 0, "ds_0 should expose at least one depth key"
+    assert len(ds_1.meta.depth_keys) > 0, "ds_1 should expose at least one depth key"
+
     aggregate_datasets(
         repo_ids=[ds_0.repo_id, ds_1.repo_id],
         roots=[ds_0.root, ds_1.root],
@@ -286,6 +340,7 @@ def test_aggregate_datasets(tmp_path, lerobot_dataset_factory):
     assert_episode_indices_updated_correctly(aggr_ds, ds_0, ds_1)
     assert_video_frames_integrity(aggr_ds, ds_0, ds_1)
     assert_video_timestamps_within_bounds(aggr_ds)
+    assert_depth_keys_preserved(aggr_ds, ds_0, ds_1)
     assert_dataset_iteration_works(aggr_ds)
 
 
@@ -403,7 +458,11 @@ def test_aggregate_incomplete_video_encoder_info_warns_and_nuls_encoders(
 
 
 def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory):
-    """Test aggregation with small file size limits to force file rotation/sharding."""
+    """Test aggregation with small file size limits to force file rotation/sharding.
+
+    Depth video features are included to verify that file rotation/concat
+    correctly handles depth-marked features alongside regular RGB ones.
+    """
     ds_0_num_episodes = ds_1_num_episodes = 10
     ds_0_num_frames = ds_1_num_frames = 400
 
@@ -412,14 +471,19 @@ def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory):
         repo_id=f"{DUMMY_REPO_ID}_small_0",
         total_episodes=ds_0_num_episodes,
         total_frames=ds_0_num_frames,
+        camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
     )
     ds_1 = lerobot_dataset_factory(
         root=tmp_path / "small_1",
         repo_id=f"{DUMMY_REPO_ID}_small_1",
         total_episodes=ds_1_num_episodes,
         total_frames=ds_1_num_frames,
+        camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
     )
 
+    assert len(ds_0.meta.depth_keys) > 0, "ds_0 should expose at least one depth key"
+    assert len(ds_1.meta.depth_keys) > 0, "ds_1 should expose at least one depth key"
+
     # Use the new configurable parameters to force file rotation
     aggregate_datasets(
         repo_ids=[ds_0.repo_id, ds_1.repo_id],
@@ -450,6 +514,7 @@ def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory):
     assert_episode_indices_updated_correctly(aggr_ds, ds_0, ds_1)
     assert_video_frames_integrity(aggr_ds, ds_0, ds_1)
     assert_video_timestamps_within_bounds(aggr_ds)
+    assert_depth_keys_preserved(aggr_ds, ds_0, ds_1)
     assert_dataset_iteration_works(aggr_ds)
 
     # Check that multiple files were actually created due to small size limits
@@ -469,7 +534,8 @@ def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory):
     """Regression test for video timestamp bug when merging datasets.
 
     This test specifically checks that video timestamps are correctly calculated
-    and accumulated when merging multiple datasets.
+    and accumulated when merging multiple datasets. Depth video features are
+    included so depth timestamps are also covered by the regression.
     """
     datasets = []
     for i in range(3):
@@ -478,9 +544,13 @@ def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory):
             repo_id=f"{DUMMY_REPO_ID}_regression_{i}",
             total_episodes=2,
             total_frames=100,
+            camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
         )
         datasets.append(ds)
 
+    for i, ds in enumerate(datasets):
+        assert len(ds.meta.depth_keys) > 0, f"Dataset {i} should expose at least one depth key"
+
     aggregate_datasets(
         repo_ids=[ds.repo_id for ds in datasets],
         roots=[ds.root for ds in datasets],
@@ -497,12 +567,21 @@ def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory):
         aggr_ds = LeRobotDataset(f"{DUMMY_REPO_ID}_regression_aggr", root=tmp_path / "regression_aggr")
 
     assert_video_timestamps_within_bounds(aggr_ds)
+    # Depth keys must survive the merge for the regression to cover the
+    # ``is_depth_map`` decoding branch.
+    assert set(aggr_ds.meta.depth_keys) == set(datasets[0].meta.depth_keys)
 
+    depth_keys = set(aggr_ds.meta.depth_keys)
     for i in range(len(aggr_ds)):
         item = aggr_ds[i]
         for key in aggr_ds.meta.video_keys:
             assert key in item, f"Video key {key} missing from item {i}"
-            assert item[key].shape[0] == 3, f"Expected 3 channels for video key {key}"
+            # Depth frames are single-channel (1, H, W) after dequantization;
+            # standard RGB frames keep the 3-channel layout.
+            expected_channels = 1 if key in depth_keys else 3
+            assert item[key].shape[0] == expected_channels, (
+                f"Expected {expected_channels} channels for video key {key}, got {item[key].shape}"
+            )
 
 
 def assert_image_schema_preserved(aggr_ds):
@@ -566,6 +645,41 @@ def assert_image_frames_integrity(aggr_ds, ds_0, ds_1):
             )
 
 
+@pytest.mark.parametrize("use_videos", [True, False], ids=["video", "image"])
+def test_aggregate_one_row_group_per_episode(tmp_path, lerobot_dataset_factory, use_videos):
+    """Aggregated DATA shards keep one row group per episode (not one collapsed group).
+
+    Covers both the non-image (``df.to_parquet``) and image
+    (``to_parquet_with_hf_images``) write branches, including the merge-into-
+    existing-file branch via a low file-size threshold that forces packing.
+    """
+    ds_0 = lerobot_dataset_factory(
+        root=tmp_path / "rg_0",
+        repo_id=f"{DUMMY_REPO_ID}_rg_0",
+        total_episodes=3,
+        total_frames=60,
+        use_videos=use_videos,
+    )
+    ds_1 = lerobot_dataset_factory(
+        root=tmp_path / "rg_1",
+        repo_id=f"{DUMMY_REPO_ID}_rg_1",
+        total_episodes=4,
+        total_frames=80,
+        use_videos=use_videos,
+    )
+
+    aggr_root = tmp_path / "rg_aggr"
+    aggregate_datasets(
+        repo_ids=[ds_0.repo_id, ds_1.repo_id],
+        roots=[ds_0.root, ds_1.root],
+        aggr_repo_id=f"{DUMMY_REPO_ID}_rg_aggr",
+        aggr_root=aggr_root,
+    )
+
+    n_episodes = assert_data_shards_one_row_group_per_episode(aggr_root)
+    assert n_episodes == ds_0.num_episodes + ds_1.num_episodes
+
+
 def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory):
     """Test aggregation of image-based datasets preserves HuggingFace Image schema.
 
@@ -584,25 +698,31 @@ def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory):
     ds_0_num_episodes = 2
     ds_1_num_episodes = 3
 
-    # Create two image-based datasets (use_videos=False)
+    # Create two image-based datasets (use_videos=False) with a mix of RGB
+    # and depth-marked cameras so the depth path is exercised in image mode.
     ds_0 = lerobot_dataset_factory(
         root=tmp_path / "image_0",
         repo_id=f"{DUMMY_REPO_ID}_image_0",
         total_episodes=ds_0_num_episodes,
         total_frames=ds_0_num_frames,
-        use_videos=False,  # Image-based dataset
+        use_videos=False,
+        camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
     )
     ds_1 = lerobot_dataset_factory(
         root=tmp_path / "image_1",
         repo_id=f"{DUMMY_REPO_ID}_image_1",
         total_episodes=ds_1_num_episodes,
         total_frames=ds_1_num_frames,
-        use_videos=False,  # Image-based dataset
+        use_videos=False,
+        camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
     )
 
     # Verify source datasets have image keys
     assert len(ds_0.meta.image_keys) > 0, "ds_0 should have image keys"
     assert len(ds_1.meta.image_keys) > 0, "ds_1 should have image keys"
+    # And that the depth marker actually made it onto an image feature.
+    assert len(ds_0.meta.depth_keys) > 0, "ds_0 should expose at least one depth key"
+    assert len(ds_1.meta.depth_keys) > 0, "ds_1 should expose at least one depth key"
 
     # Aggregate the datasets
     aggregate_datasets(
@@ -637,6 +757,7 @@ def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory):
     # Image-specific assertions
     assert_image_schema_preserved(aggr_ds)
     assert_image_frames_integrity(aggr_ds, ds_0, ds_1)
+    assert_depth_keys_preserved(aggr_ds, ds_0, ds_1)
 
     # Verify images can be accessed and have correct shape
     sample_item = aggr_ds[0]

tests/datasets/test_dataset_metadata.py

@@ -59,11 +59,13 @@ def _make_dummy_stats(features: dict) -> dict:
     stats = {}
     for key, ft in features.items():
         if ft["dtype"] in ("image", "video"):
+            channels = ft["shape"][-1]
+            stat_shape = (channels, 1, 1)
             stats[key] = {
-                "max": np.ones((3, 1, 1), dtype=np.float32),
+                "max": np.ones(stat_shape, dtype=np.float32),
-                "mean": np.full((3, 1, 1), 0.5, dtype=np.float32),
+                "mean": np.full(stat_shape, 0.5, dtype=np.float32),
-                "min": np.zeros((3, 1, 1), dtype=np.float32),
+                "min": np.zeros(stat_shape, dtype=np.float32),
-                "std": np.full((3, 1, 1), 0.25, dtype=np.float32),
+                "std": np.full(stat_shape, 0.25, dtype=np.float32),
                 "count": np.array([5]),
             }
         elif ft["dtype"] in ("float32", "float64", "int64"):
@@ -142,6 +144,45 @@ def test_create_without_videos_has_no_video_path(tmp_path):
     assert meta.video_keys == []
 
 
+@pytest.mark.parametrize(
+    ("marker_field", "marker_key"),
+    [
+        ("info", "is_depth_map"),
+        ("info", "video.is_depth_map"),
+        ("video_info", "video.is_depth_map"),
+    ],
+    ids=["info.is_depth_map", "info.video.is_depth_map_legacy", "video_info.video.is_depth_map_legacy"],
+)
+def test_depth_keys_property_filters_by_marker(tmp_path, marker_field, marker_key):
+    """``depth_keys`` recognises the canonical and the two legacy marker variants."""
+    depth_feature = {
+        "dtype": "video",
+        "shape": (64, 96, 1),
+        "names": ["height", "width", "channels"],
+        marker_field: {marker_key: True},
+    }
+    features = {
+        **VIDEO_FEATURES,
+        "observation.images.laptop_depth": depth_feature,
+    }
+    meta = LeRobotDatasetMetadata.create(
+        repo_id="test/depth_keys",
+        fps=DEFAULT_FPS,
+        features=features,
+        root=tmp_path / f"depth_keys_{marker_field}_{marker_key.replace('.', '_')}",
+    )
+
+    assert set(meta.video_keys) == {"observation.images.laptop", "observation.images.laptop_depth"}
+    assert meta.depth_keys == ["observation.images.laptop_depth"]
+
+
+def test_depth_keys_empty_when_no_marker(tmp_path):
+    meta = LeRobotDatasetMetadata.create(
+        repo_id="test/no_depth", fps=DEFAULT_FPS, features=VIDEO_FEATURES, root=tmp_path / "no_depth"
+    )
+    assert meta.depth_keys == []
+
+
 def test_create_raises_on_existing_directory(tmp_path):
     """create() raises if root directory already exists."""
     root = tmp_path / "existing"

tests/datasets/test_dataset_tools.py

@@ -24,7 +24,7 @@ import torch
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 
 
-from lerobot.configs import VideoEncoderConfig
+from lerobot.configs import DepthEncoderConfig, VideoEncoderConfig
 from lerobot.datasets.dataset_tools import (
     add_features,
     convert_image_to_video_dataset,
@@ -37,7 +37,9 @@ from lerobot.datasets.dataset_tools import (
     split_dataset,
 )
 from lerobot.datasets.io_utils import load_info
-from tests.datasets.test_video_encoding import _add_frames, require_h264, require_libsvtav1
+from tests.datasets.test_video_encoding import require_h264, require_hevc, require_libsvtav1
+from tests.fixtures.constants import DUMMY_DEPTH_FEATURES, DUMMY_DEPTH_KEY
+from tests.fixtures.dataset_factories import add_frames
 
 
 @pytest.fixture
@@ -1332,9 +1334,131 @@ def test_convert_image_to_video_dataset_subset_episodes(tmp_path):
             shutil.rmtree(output_dir)
 
 
+@require_libsvtav1
+@require_hevc
+def test_convert_image_to_video_dataset_depth(tmp_path, empty_lerobot_dataset_factory):
+    """Depth image features convert to depth videos using the depth encoder.
+
+    Mirrors :func:`test_convert_image_to_video_dataset` but with a small local
+    image dataset that mixes an RGB camera with a depth camera, so the
+    ``depth_keys`` → ``depth_encoder`` routing and ``is_depth_map`` preservation
+    are exercised end-to-end.
+    """
+    features = {
+        "action": {"dtype": "float32", "shape": (2,), "names": ["a", "b"]},
+        "observation.images.cam": {
+            "dtype": "image",
+            "shape": (64, 96, 3),
+            "names": ["height", "width", "channels"],
+        },
+        "observation.images.depth": {
+            "dtype": "image",
+            "shape": (64, 96, 1),
+            "names": ["height", "width", "channels"],
+            "info": {"is_depth_map": True},
+        },
+    }
+    source_dataset = empty_lerobot_dataset_factory(
+        root=tmp_path / "img_ds",
+        features=features,
+        use_videos=False,
+    )
+
+    add_frames(source_dataset, num_frames=4)
+    source_dataset.save_episode()
+    source_dataset.finalize()
+
+    # Source is an image dataset with the depth marker on the depth camera.
+    assert len(source_dataset.meta.video_keys) == 0
+    assert "observation.images.depth" in source_dataset.meta.depth_keys
+
+    output_dir = tmp_path / "video_ds"
+    with (
+        patch("lerobot.datasets.dataset_metadata.get_safe_version") as mock_get_safe_version,
+        patch("lerobot.datasets.dataset_metadata.snapshot_download") as mock_snapshot_download,
+    ):
+        mock_get_safe_version.return_value = "v3.0"
+        mock_snapshot_download.return_value = str(output_dir)
+
+        # Use non-default quantization params so the persisted metadata must
+        # come from the depth encoder (not RGB encoder defaults).
+        depth_encoder = DepthEncoderConfig(
+            vcodec="hevc",
+            pix_fmt="gray12le",
+            g=2,
+            crf=30,
+            depth_min=0.05,
+            depth_max=8.0,
+            shift=2.0,
+            use_log=False,
+        )
+        video_dataset = convert_image_to_video_dataset(
+            dataset=source_dataset,
+            output_dir=output_dir,
+            repo_id="dummy/depth_video",
+            camera_encoder=VideoEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
+            depth_encoder=depth_encoder,
+            num_workers=1,
+        )
+
+    # Both cameras are now videos, and the depth marker survived the conversion.
+    assert "observation.images.cam" in video_dataset.meta.video_keys
+    assert "observation.images.depth" in video_dataset.meta.video_keys
+    assert "observation.images.depth" in video_dataset.meta.depth_keys
+    assert "observation.images.cam" not in video_dataset.meta.depth_keys
+
+    depth_path = video_dataset.root / video_dataset.meta.get_video_file_path(0, "observation.images.depth")
+    assert depth_path.exists(), f"Depth video file should exist: {depth_path}"
+
+    # The persisted depth-video metadata must carry the depth quantization params
+    # from the depth encoder (so frames dequantize correctly on read), and the RGB
+    # camera must not be marked as a depth map.
+    persisted_info = load_info(video_dataset.root)
+    depth_info = persisted_info.features["observation.images.depth"]["info"]
+    assert depth_info["is_depth_map"] is True
+    assert DepthEncoderConfig.from_video_info(depth_info) == depth_encoder
+
+    cam_info = persisted_info.features["observation.images.cam"]["info"]
+    assert cam_info.get("is_depth_map") is False
+    assert "video.codec" in cam_info
+
+
 # ─── reencode_dataset ─────────────────────────────────────────────────
 
 
+@require_hevc
+def test_reencode_dataset_depth_uses_depth_encoder(tmp_path, empty_lerobot_dataset_factory):
+    """Depth videos are re-encoded with the depth encoder and keep their depth metadata.
+
+    Depth-focused companion to :func:`test_reencode_dataset_multi_key_multiprocessing`.
+    """
+    initial_cfg = DepthEncoderConfig(vcodec="hevc", pix_fmt="gray12le", g=2, crf=30)
+    dataset = empty_lerobot_dataset_factory(
+        root=tmp_path / "ds",
+        features=DUMMY_DEPTH_FEATURES,
+        use_videos=True,
+        depth_encoder=initial_cfg,
+    )
+
+    add_frames(dataset, num_frames=4)
+    dataset.save_episode()
+    dataset.finalize()
+
+    assert DUMMY_DEPTH_KEY in dataset.meta.depth_keys
+
+    target_cfg = DepthEncoderConfig(vcodec="hevc", pix_fmt="gray12le", g=6, crf=23)
+    result = reencode_dataset(dataset, depth_encoder=target_cfg, num_workers=0)
+
+    assert result is dataset
+
+    persisted_info = load_info(dataset.root)
+    depth_info = persisted_info.features[DUMMY_DEPTH_KEY].get("info", {})
+    # Re-encode applied the new codec parameters to the depth video ...
+    assert DepthEncoderConfig.from_video_info(depth_info) == target_cfg
+    # ... while preserving the depth marker.
+    assert depth_info["is_depth_map"] is True
+
+
 @require_libsvtav1
 @require_h264
 def test_reencode_dataset_multi_key_multiprocessing(
@@ -1350,9 +1474,9 @@ def test_reencode_dataset_multi_key_multiprocessing(
         camera_encoder=initial_cfg,
     )
 
-    _add_frames(dataset, num_frames=4)
+    add_frames(dataset, num_frames=4)
     dataset.save_episode()
-    _add_frames(dataset, num_frames=4)
+    add_frames(dataset, num_frames=4)
     dataset.save_episode()
     dataset.finalize()
 

tests/datasets/test_dataset_writer.py

@@ -53,8 +53,8 @@ def _make_frame(features: dict, task: str = "Dummy task") -> dict:
 # ── Existing encode_video_worker tests ───────────────────────────────
 
 
-def test_encode_video_worker_forwards_camera_encoder(tmp_path):
+def test_encode_video_worker_forwards_video_encoder(tmp_path):
-    """_encode_video_worker forwards camera_encoder to encode_video_frames."""
+    """_encode_video_worker forwards video_encoder to encode_video_frames."""
     video_key = "observation.images.laptop"
     fpath = DEFAULT_IMAGE_PATH.format(image_key=video_key, episode_index=0, frame_index=0)
     img_dir = tmp_path / Path(fpath).parent
@@ -74,16 +74,16 @@ def test_encode_video_worker_forwards_camera_encoder(tmp_path):
             0,
             tmp_path,
             fps=30,
-            camera_encoder=VideoEncoderConfig(vcodec="h264", preset=None),
+            video_encoder=VideoEncoderConfig(vcodec="h264", preset=None),
             encoder_threads=4,
         )
 
-    assert captured_kwargs["camera_encoder"].vcodec == "h264"
+    assert captured_kwargs["video_encoder"].vcodec == "h264"
     assert captured_kwargs["encoder_threads"] == 4
 
 
-def test_encode_video_worker_default_camera_encoder(tmp_path):
+def test_encode_video_worker_default_video_encoder(tmp_path):
-    """_encode_video_worker passes None camera_encoder which encode_video_frames defaults."""
+    """_encode_video_worker passes None video_encoder which encode_video_frames defaults."""
     video_key = "observation.images.laptop"
     fpath = DEFAULT_IMAGE_PATH.format(image_key=video_key, episode_index=0, frame_index=0)
     img_dir = tmp_path / Path(fpath).parent
@@ -100,7 +100,7 @@ def test_encode_video_worker_default_camera_encoder(tmp_path):
     with patch("lerobot.datasets.dataset_writer.encode_video_frames", side_effect=mock_encode):
         _encode_video_worker(video_key, 0, tmp_path, fps=30)
 
-    assert captured_kwargs["camera_encoder"] is None
+    assert captured_kwargs["video_encoder"] is None
     assert captured_kwargs["encoder_threads"] is None
 
 

tests/datasets/test_datasets.py

@@ -51,7 +51,7 @@ from lerobot.robots import make_robot_from_config
 from lerobot.transforms import ImageTransforms, ImageTransformsConfig
 from lerobot.utils.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, OBS_STR, REWARD
 from lerobot.utils.feature_utils import hw_to_dataset_features
-from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_REPO_ID
+from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_MOTOR_FEATURES, DUMMY_REPO_ID
 from tests.mocks.mock_robot import MockRobotConfig
 from tests.utils import require_x86_64_kernel
 
@@ -133,6 +133,21 @@ def test_dataset_feature_with_forward_slash_raises_error():
         )
 
 
+def test_create_does_not_mutate_input_features(tmp_path, empty_lerobot_dataset_factory):
+    # ``create`` must deep-copy features so a dataset built from another's features stays independent.
+    dataset = empty_lerobot_dataset_factory(
+        root=tmp_path / "ds1", features=DUMMY_MOTOR_FEATURES, use_videos=False
+    )
+    dataset_copy = empty_lerobot_dataset_factory(
+        root=tmp_path / "ds2", features=dataset.meta.features, use_videos=False
+    )
+
+    original_shape = dataset.meta.info.features["state"]["shape"]
+    dataset_copy.meta.info.features["state"]["shape"] = (999,)
+
+    assert dataset.meta.info.features["state"]["shape"] == original_shape
+
+
 def test_add_frame_missing_task(tmp_path, empty_lerobot_dataset_factory):
     features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
     dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)
@@ -1516,10 +1531,15 @@ def test_valid_video_codecs_constant():
     assert "h264" in VALID_VIDEO_CODECS
     assert "hevc" in VALID_VIDEO_CODECS
     assert "libsvtav1" in VALID_VIDEO_CODECS
+    assert "ffv1" in VALID_VIDEO_CODECS
     assert "auto" in VALID_VIDEO_CODECS
     assert "h264_videotoolbox" in VALID_VIDEO_CODECS
     assert "h264_nvenc" in VALID_VIDEO_CODECS
-    assert len(VALID_VIDEO_CODECS) == 10
+    assert "h264_vaapi" in VALID_VIDEO_CODECS
+    assert "h264_qsv" in VALID_VIDEO_CODECS
+    assert "hevc_videotoolbox" in VALID_VIDEO_CODECS
+    assert "hevc_nvenc" in VALID_VIDEO_CODECS
+    assert len(VALID_VIDEO_CODECS) == 11
 
 
 def test_delta_timestamps_with_episodes_filter(tmp_path, empty_lerobot_dataset_factory):

tests/datasets/test_depth.py

@@ -0,0 +1,241 @@
+"""Tests for the depth-integration feature.
+
+Covers:
+- ``depth_utils`` quantize/dequantize round-trips and backend agreement.
+- Image-writer support for single-channel depth.
+- Hardware-feature → depth flag routing.
+- Feature-to-file-format routing through the dataset writer.
+
+Depth metadata detection on ``LeRobotDatasetMetadata.depth_keys`` lives in
+``test_dataset_metadata.py``. Depth video encoding/decoding lives in
+``test_video_encoding.py``.
+"""
+
+from pathlib import Path
+
+import pytest
+
+pytest.importorskip("av", reason="av is required (install lerobot[dataset])")
+
+import av
+import numpy as np
+import PIL.Image
+import torch
+
+from lerobot.configs import DepthEncoderConfig
+from lerobot.configs.video import DEFAULT_DEPTH_MAX, DEFAULT_DEPTH_MIN, DEPTH_QMAX
+from lerobot.datasets.depth_utils import dequantize_depth, quantize_depth
+from lerobot.datasets.image_writer import image_array_to_pil_image, write_image
+from tests.fixtures.constants import (
+    DEFAULT_FPS,
+    DUMMY_CAMERA_FEATURES,
+    DUMMY_CAMERA_FEATURES_WITH_DEPTH,
+    DUMMY_CHW,
+    DUMMY_DEPTH_CAMERA_FEATURES,
+    DUMMY_REPO_ID,
+)
+from tests.fixtures.dataset_factories import add_frames
+
+_, H, W = DUMMY_CHW
+
+
+def _depth_metres_ramp() -> np.ndarray:
+    """Linearly-spaced float32 depth in metres covering the default range."""
+    return np.linspace(DEFAULT_DEPTH_MIN, DEFAULT_DEPTH_MAX, H * W, dtype=np.float32).reshape(H, W)
+
+
+# ── 1. Quantize / dequantize round-trips ──────────────────────────────
+
+
+class TestQuantizeDequantize:
+    """Numerical contract of ``quantize_depth`` / ``dequantize_depth``."""
+
+    @pytest.mark.parametrize("use_log", [False, True])
+    @pytest.mark.parametrize("output_unit", ["m", "mm"])
+    @pytest.mark.parametrize("output_channel_last", [False, True])
+    def test_roundtrip(self, use_log, output_unit, output_channel_last):
+        """quantize → dequantize recovers depth; layout and unit are honored."""
+        depth = _depth_metres_ramp()
+        quantized = quantize_depth(depth, use_log=use_log, video_backend=None)
+        recovered = dequantize_depth(
+            quantized,
+            use_log=use_log,
+            output_unit=output_unit,
+            output_tensor=False,
+            output_channel_last=output_channel_last,
+        )
+
+        expected_shape = (H, W, 1) if output_channel_last else (1, H, W)
+        assert recovered.shape == expected_shape
+
+        recovered_m = recovered.astype(np.float32)
+        if output_unit == "mm":
+            recovered_m = recovered_m / 1000.0
+        recovered_2d = recovered_m[..., 0] if output_channel_last else recovered_m[0]
+
+        if use_log:
+            # Log mode: tighter near-range error than far-range (the whole point).
+            near = depth < 1.0
+            far = depth > 8.0
+            err_near = np.abs(recovered_2d[near] - depth[near])
+            err_far = np.abs(recovered_2d[far] - depth[far])
+            assert err_near.mean() < err_far.mean()
+        else:
+            # Linear mode: bounded by quant step + 1 mm of unit-conversion rounding.
+            tol = (DEFAULT_DEPTH_MAX - DEFAULT_DEPTH_MIN) / DEPTH_QMAX + 1e-3
+            np.testing.assert_allclose(recovered_2d, depth, atol=tol)
+
+    @pytest.mark.parametrize("use_log", [False, True])
+    @pytest.mark.parametrize("output_unit", ["m", "mm"])
+    def test_numpy_torch_agree(self, use_log, output_unit):
+        """Batched torch path produces the same values as the numpy path."""
+        batch_size = 3
+        per_frame = np.linspace(0, DEPTH_QMAX, H * W, dtype=np.uint16).reshape(H, W)
+        batch_np = np.broadcast_to(per_frame[None, None, ...], (batch_size, 1, H, W)).copy()
+        batch_t = torch.from_numpy(batch_np.astype(np.int32))  # torch.uint16 support is patchy.
+
+        ref = dequantize_depth(batch_np, use_log=use_log, output_unit=output_unit, output_tensor=False)
+        out = dequantize_depth(batch_t, use_log=use_log, output_unit=output_unit, output_tensor=True)
+
+        assert isinstance(out, torch.Tensor)
+        assert out.shape == (batch_size, 1, H, W)
+        # ``m``: float32 noise (~10 µm in log mode, after ``exp``) — still 200× below the ~2 mm quant step.
+        # ``mm`` + tensor stays in float32 (no uint16 round-trip), so allow 1 mm slop.
+        atol = 1e-5 if output_unit == "m" else 1.0
+        np.testing.assert_allclose(out.cpu().numpy().astype(np.float64), ref.astype(np.float64), atol=atol)
+
+    @pytest.mark.parametrize(
+        "input_shape,output_shape",
+        [
+            ((H, W), (1, H, W)),
+            ((1, H, W), (1, H, W)),
+            ((H, W, 1), (1, H, W)),
+            ((3, 1, H, W), (3, 1, H, W)),
+            ((3, H, W, 1), (3, 1, H, W)),
+        ],
+    )
+    def test_input_layouts_accepted(self, input_shape, output_shape):
+        """All documented input layouts decode to the channel-first default."""
+        quantized = np.full(input_shape, DEPTH_QMAX // 2, dtype=np.uint16)
+        out = dequantize_depth(quantized, output_unit="m", output_tensor=False)
+        assert out.shape == output_shape
+
+    def test_pyav_frame_roundtrip(self):
+        """quantize → av.VideoFrame → dequantize works."""
+        depth = _depth_metres_ramp()
+        frame = quantize_depth(depth, use_log=False, video_backend="pyav")
+        assert isinstance(frame, av.VideoFrame)
+
+        recovered = dequantize_depth(frame, use_log=False, output_unit="m", output_tensor=False)
+        assert recovered.shape == (1, H, W)
+        tol = (DEFAULT_DEPTH_MAX - DEFAULT_DEPTH_MIN) / DEPTH_QMAX + 1e-3
+        np.testing.assert_allclose(recovered[0], depth, atol=tol)
+
+    def test_invalid_log_params_raises(self):
+        with pytest.raises(ValueError, match=r"depth_min \+ shift must be positive"):
+            quantize_depth(_depth_metres_ramp(), depth_min=1.0, shift=-2.0, use_log=True, video_backend=None)
+
+
+# ── 2. Image writer depth support ─────────────────────────────────────
+
+
+class TestImageWriterDepth:
+    """``image_array_to_pil_image`` and ``write_image`` for depth maps."""
+
+    @pytest.mark.parametrize("dtype,expected_mode", [(np.uint16, "I;16"), (np.float32, "F")])
+    @pytest.mark.parametrize("shape", [(H, W), (H, W, 1), (1, H, W)])
+    def test_pil_depth_modes_and_squeeze(self, dtype, expected_mode, shape):
+        """Single-channel depth converts to PIL with the right mode and (W, H) size."""
+        arr = np.zeros(shape, dtype=dtype)
+        img = image_array_to_pil_image(arr)
+        assert img.mode == expected_mode
+        assert img.size == (W, H)
+
+    def test_write_image_tiff_roundtrip(self, tmp_path):
+        """uint16 depth round-trips through .tiff."""
+        arr = np.arange(H * W, dtype=np.uint16).reshape(H, W)
+        fpath = tmp_path / "depth.tiff"
+        write_image(arr, fpath)
+        with PIL.Image.open(fpath) as loaded:
+            recovered = np.array(loaded)
+        np.testing.assert_array_equal(recovered, arr)
+
+
+# ── 3. Hardware-feature → depth flag ──────────────────────────────────
+
+
+class TestHwToDatasetFeaturesDepth:
+    """``hw_to_dataset_features`` flags single-channel cameras as depth."""
+
+    @pytest.mark.parametrize("channels,is_depth", [(1, True), (3, False)])
+    def test_depth_marker_by_channels(self, channels, is_depth):
+        from lerobot.utils.feature_utils import hw_to_dataset_features
+
+        features = hw_to_dataset_features({"cam": (480, 640, channels)}, prefix="observation")
+        assert features["observation.images.cam"]["info"]["is_depth_map"] is is_depth
+
+    def test_invalid_channel_count_raises(self):
+        from lerobot.utils.feature_utils import hw_to_dataset_features
+
+        with pytest.raises(ValueError, match="Expected a 3-tuple"):
+            hw_to_dataset_features({"cam": (480, 640, 2)}, prefix="observation")
+
+
+# ── 4. Feature-to-file-format routing ────────────────────────────────
+
+
+# Keys derived from DUMMY_CAMERA_FEATURES_WITH_DEPTH; pick one RGB and the depth camera.
+RGB_KEY = next(iter(DUMMY_CAMERA_FEATURES))
+DEPTH_KEY = next(iter(DUMMY_DEPTH_CAMERA_FEATURES))
+
+
+class TestFeatureFileRouting:
+    """Depth vs RGB features route to the correct file format."""
+
+    NUM_FRAMES = 5
+
+    def test_image_mode_depth_tiff_rgb_png(self, tmp_path, features_factory):
+        """Without video encoding: depth → .tiff, RGB → .png."""
+        from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+        features = features_factory(camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH, use_videos=False)
+        dataset = LeRobotDataset.create(
+            repo_id=DUMMY_REPO_ID,
+            fps=DEFAULT_FPS,
+            features=features,
+            root=tmp_path / "ds",
+            use_videos=False,
+        )
+
+        add_frames(dataset, num_frames=self.NUM_FRAMES)
+
+        buf = dataset.writer.episode_buffer
+        assert all(Path(p).suffix == ".tiff" for p in buf[DEPTH_KEY])
+        assert all(Path(p).suffix == ".png" for p in buf[RGB_KEY])
+
+        dataset.save_episode()
+        dataset.finalize()
+
+    def test_video_mode_depth_uses_depth_encoder(self, tmp_path, features_factory):
+        """With streaming video encoding: depth → DepthEncoderConfig, RGB does not."""
+        from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+        features = features_factory(camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH, use_videos=True)
+        dataset = LeRobotDataset.create(
+            repo_id=DUMMY_REPO_ID,
+            fps=DEFAULT_FPS,
+            features=features,
+            root=tmp_path / "ds",
+            use_videos=True,
+            streaming_encoding=True,
+        )
+
+        add_frames(dataset, num_frames=self.NUM_FRAMES)
+
+        encoder = dataset.writer._streaming_encoder
+        assert encoder is not None
+        assert isinstance(encoder._threads[DEPTH_KEY].video_encoder, DepthEncoderConfig)
+        assert not isinstance(encoder._threads[RGB_KEY].video_encoder, DepthEncoderConfig)
+
+        dataset.save_episode()
+        dataset.finalize()

tests/datasets/test_image_writer.py

@@ -94,7 +94,7 @@ def test_image_array_to_pil_image_pytorch_format(img_array_factory):
 
 def test_image_array_to_pil_image_single_channel(img_array_factory):
     img_array = img_array_factory(channels=1)
-    with pytest.raises(NotImplementedError):
+    with pytest.raises(ValueError, match="Unsupported single-channel image dtype"):
         image_array_to_pil_image(img_array)
 
 

tests/datasets/test_streaming_video_encoder.py

@@ -61,9 +61,7 @@ class TestCameraEncoderThread:
         encoder_thread = _CameraEncoderThread(
             video_path=video_path,
             fps=fps,
-            vcodec=enc_cfg.vcodec,
+            video_encoder=enc_cfg,
-            pix_fmt=enc_cfg.pix_fmt,
-            codec_options=enc_cfg.get_codec_options(as_strings=True),
             frame_queue=frame_queue,
             result_queue=result_queue,
             stop_event=stop_event,
@@ -112,9 +110,7 @@ class TestCameraEncoderThread:
         encoder_thread = _CameraEncoderThread(
             video_path=video_path,
             fps=fps,
-            vcodec=enc_cfg.vcodec,
+            video_encoder=enc_cfg,
-            pix_fmt=enc_cfg.pix_fmt,
-            codec_options=enc_cfg.get_codec_options(as_strings=True),
             frame_queue=frame_queue,
             result_queue=result_queue,
             stop_event=stop_event,
@@ -146,9 +142,7 @@ class TestCameraEncoderThread:
         encoder_thread = _CameraEncoderThread(
             video_path=video_path,
             fps=fps,
-            vcodec=enc_cfg.vcodec,
+            video_encoder=enc_cfg,
-            pix_fmt=enc_cfg.pix_fmt,
-            codec_options=enc_cfg.get_codec_options(as_strings=True),
             frame_queue=frame_queue,
             result_queue=result_queue,
             stop_event=stop_event,
@@ -391,7 +385,8 @@ class TestStreamingVideoEncoder:
 
         # Verify codec options include thread tuning for libsvtav1 (lp=…)
         thread = encoder._threads[f"{OBS_IMAGES}.cam"]
-        assert "svtav1-params" in thread.codec_options or "threads" in thread.codec_options
+        codec_opts = thread.video_encoder.get_codec_options(encoder_threads=thread.encoder_threads)
+        assert "svtav1-params" in codec_opts or "threads" in codec_opts
 
         # Feed some frames and finish to ensure it works end-to-end
         num_frames = 10

tests/datasets/test_video_encoding.py

@@ -26,7 +26,7 @@ pytest.importorskip("av", reason="av is required (install lerobot[dataset])")
 
 import av  # noqa: E402
 
-from lerobot.configs import VALID_VIDEO_CODECS, VideoEncoderConfig
+from lerobot.configs import VALID_VIDEO_CODECS, DepthEncoderConfig, VideoEncoderConfig
 from lerobot.datasets.image_writer import write_image
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.pyav_utils import get_codec
@@ -37,7 +37,15 @@ from lerobot.datasets.video_utils import (
     get_video_info,
     reencode_video,
 )
-from tests.fixtures.constants import DUMMY_VIDEO_INFO
+from tests.fixtures.constants import (
+    DUMMY_DEPTH_FEATURES,
+    DUMMY_DEPTH_KEY,
+    DUMMY_DEPTH_VIDEO_INFO_FULL,
+    DUMMY_VIDEO_FEATURES,
+    DUMMY_VIDEO_INFO,
+    DUMMY_VIDEO_KEY,
+)
+from tests.fixtures.dataset_factories import add_frames
 
 
 # Per-codec skip markers — validation tests only fire when the codec is available
@@ -48,12 +56,67 @@ def _require_encoder(vcodec: str) -> pytest.MarkDecorator:
 
 require_libsvtav1 = _require_encoder("libsvtav1")
 require_h264 = _require_encoder("h264")
+require_hevc = _require_encoder("hevc")
 require_videotoolbox = _require_encoder("h264_videotoolbox")
 require_nvenc = _require_encoder("h264_nvenc")
 require_vaapi = _require_encoder("h264_vaapi")
 require_qsv = _require_encoder("h264_qsv")
 
 
+TEST_ARTIFACTS_DIR = Path(__file__).parent.parent / "artifacts" / "encoded_videos"
+
+
+def _write_color_frames(imgs_dir: Path, num_frames: int = 4, height: int = 64, width: int = 96) -> None:
+    imgs_dir.mkdir(parents=True, exist_ok=True)
+    for i in range(num_frames):
+        arr = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
+        write_image(arr, imgs_dir / f"frame-{i:06d}.png")
+
+
+def _write_depth_frames(imgs_dir: Path, num_frames: int = 4, height: int = 64, width: int = 96) -> None:
+    """Write synthetic uint16 depth TIFFs (millimetres) for depth encoder tests.
+
+    Uses a smooth linear ramp + per-frame offset (not white noise) so HEVC Main 12
+    on ``gray12le`` compresses well. Values span ~100 mm to 10 m, covering most
+    of the default ``[DEPTH_MIN, DEPTH_MAX]`` metres range after
+    ``quantize_depth(input_unit="auto"="mm")``.
+    """
+    imgs_dir.mkdir(parents=True, exist_ok=True)
+    base = np.linspace(100.0, 10_000.0, height * width, dtype=np.float32).reshape(height, width)
+    for i in range(num_frames):
+        arr = (base + 50.0 * i).clip(0, 65535).astype(np.uint16)
+        write_image(arr, imgs_dir / f"frame-{i:06d}.tiff")
+
+
+def _encode_video(
+    path: Path,
+    num_frames: int = 4,
+    fps: int = 30,
+    cfg: VideoEncoderConfig | None = None,
+    depth: bool = False,
+) -> Path:
+    """Write synthetic frames to a temp dir and encode them to ``path``.
+
+    ``depth=False`` writes uint8 RGB PNG noise and encodes with ``cfg``
+    (defaulting to the library default). ``depth=True`` writes synthetic uint16
+    depth TIFFs and encodes with ``cfg`` or a default :class:`DepthEncoderConfig`
+    (HEVC Main 12 / ``gray12le``).
+    """
+    imgs_dir = path.parent / f"imgs_{path.stem}"
+    if depth:
+        _write_depth_frames(imgs_dir, num_frames=num_frames)
+        cfg = cfg or DepthEncoderConfig()
+    else:
+        _write_color_frames(imgs_dir, num_frames=num_frames)
+    encode_video_frames(imgs_dir, path, fps=fps, video_encoder=cfg, overwrite=True)
+    return path
+
+
+def _read_feature_info(dataset: LeRobotDataset, key: str = DUMMY_VIDEO_KEY) -> dict:
+    info = json.loads((dataset.root / INFO_PATH).read_text())
+    return info["features"][key]["info"]
+
+
 # ─── VideoEncoderConfig / codec options ──────────────────────────────
 
 
@@ -87,7 +150,7 @@ class TestCodecOptions:
         assert opts["q:v"] == 40
         assert "crf" not in opts
 
-    @_require_encoder("h264_nvenc")
+    @require_nvenc
     def test_nvenc_options(self):
         cfg = VideoEncoderConfig(vcodec="h264_nvenc", g=2, crf=25, preset=None)
         opts = cfg.get_codec_options()
@@ -96,12 +159,12 @@ class TestCodecOptions:
         assert "crf" not in opts
         assert opts["g"] == 2
 
-    @_require_encoder("h264_vaapi")
+    @require_vaapi
     def test_vaapi_options(self):
         cfg = VideoEncoderConfig(vcodec="h264_vaapi", crf=28, preset=None)
         assert cfg.get_codec_options()["qp"] == 28
 
-    @_require_encoder("h264_qsv")
+    @require_qsv
     def test_qsv_options(self):
         cfg = VideoEncoderConfig(vcodec="h264_qsv", crf=25, preset=None)
         assert cfg.get_codec_options()["global_quality"] == 25
@@ -313,59 +376,6 @@ class TestEncoderDetection:
         assert "h264_nvenc" in VALID_VIDEO_CODECS
 
 
-TEST_ARTIFACTS_DIR = Path(__file__).parent.parent / "artifacts" / "encoded_videos"
-
-# Default video feature set used by persistence tests.
-VIDEO_FEATURES = {
-    "observation.images.cam": {
-        "dtype": "video",
-        "shape": (64, 96, 3),
-        "names": ["height", "width", "channels"],
-    },
-    "action": {"dtype": "float32", "shape": (2,), "names": ["a", "b"]},
-}
-VIDEO_KEY = "observation.images.cam"
-
-
-def _write_frames(imgs_dir: Path, num_frames: int = 4, height: int = 64, width: int = 96) -> None:
-    imgs_dir.mkdir(parents=True, exist_ok=True)
-    for i in range(num_frames):
-        arr = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
-        write_image(arr, imgs_dir / f"frame-{i:06d}.png")
-
-
-def _encode_video(
-    path: Path, num_frames: int = 4, fps: int = 30, cfg: VideoEncoderConfig | None = None
-) -> Path:
-    imgs_dir = path.parent / f"imgs_{path.stem}"
-    _write_frames(imgs_dir, num_frames=num_frames)
-    encode_video_frames(imgs_dir, path, fps=fps, camera_encoder=cfg, overwrite=True)
-    return path
-
-
-def _read_feature_info(dataset: LeRobotDataset) -> dict:
-    info = json.loads((dataset.root / INFO_PATH).read_text())
-    return info["features"][VIDEO_KEY]["info"]
-
-
-def _add_frames(dataset: LeRobotDataset, num_frames: int, video_keys: list[str] | None = None) -> None:
-    from lerobot.utils.constants import DEFAULT_FEATURES
-
-    if video_keys is None:
-        video_keys = dataset.meta.video_keys
-    for _ in range(num_frames):
-        frame: dict = {"task": "test"}
-        for key, ft in dataset.meta.features.items():
-            if key in DEFAULT_FEATURES:
-                continue
-            shape = ft["shape"]
-            if key in video_keys:
-                frame[key] = np.random.randint(0, 256, shape, dtype=np.uint8)
-            else:
-                frame[key] = np.zeros(shape, dtype=np.float32)
-        dataset.add_frame(frame)
-
-
 class TestGetVideoInfo:
     def test_returns_all_stream_fields(self):
         info = get_video_info(TEST_ARTIFACTS_DIR / "clip_4frames.mp4")
@@ -375,7 +385,7 @@ class TestGetVideoInfo:
         assert info["video.pix_fmt"] == "yuv420p"
         assert info["video.fps"] == 30
         assert info["video.channels"] == 3
-        assert info["video.is_depth_map"] is False
+        assert info["is_depth_map"] is False
         assert info["has_audio"] is False
         assert "video.g" not in info
         assert "video.crf" not in info
@@ -385,7 +395,7 @@ class TestGetVideoInfo:
     def test_merges_encoder_config_as_video_prefixed_entries(self):
         cfg = VideoEncoderConfig(vcodec="libsvtav1", g=2, crf=30, preset=12)
 
-        info = get_video_info(TEST_ARTIFACTS_DIR / "clip_4frames.mp4", camera_encoder=cfg)
+        info = get_video_info(TEST_ARTIFACTS_DIR / "clip_4frames.mp4", video_encoder=cfg)
 
         assert info["video.g"] == 2
         assert info["video.crf"] == 30
@@ -398,11 +408,16 @@ class TestGetVideoInfo:
     def test_stream_derived_keys_take_precedence_over_config(self):
         cfg = VideoEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p")
 
-        info = get_video_info(TEST_ARTIFACTS_DIR / "clip_4frames.mp4", camera_encoder=cfg)
+        info = get_video_info(TEST_ARTIFACTS_DIR / "clip_4frames.mp4", video_encoder=cfg)
 
         assert info["video.codec"]  # populated from stream, not from config's vcodec
         assert info["video.pix_fmt"] == "yuv420p"
 
+    def test_depth_encoder_config_sets_is_depth_map_true(self):
+        """A ``DepthEncoderConfig`` causes ``get_video_info`` to mark the stream as depth."""
+        info = get_video_info(TEST_ARTIFACTS_DIR / "clip_4frames.mp4", video_encoder=DepthEncoderConfig())
+        assert info["is_depth_map"] is True
+
 
 class TestEncodeVideoFrames:
     @require_libsvtav1
@@ -434,7 +449,7 @@ class TestEncodeVideoFrames:
 
     def test_overwrite_false_skips_existing_file(self, tmp_path):
         imgs_dir = tmp_path / "imgs"
-        _write_frames(imgs_dir)
+        _write_color_frames(imgs_dir)
         video_path = tmp_path / "out.mp4"
         sentinel = b"pre-existing content"
         video_path.write_bytes(sentinel)
@@ -446,7 +461,7 @@ class TestEncodeVideoFrames:
     @require_libsvtav1
     def test_overwrite_true_replaces_existing_file(self, tmp_path):
         imgs_dir = tmp_path / "imgs"
-        _write_frames(imgs_dir)
+        _write_color_frames(imgs_dir)
         video_path = tmp_path / "out.mp4"
         video_path.write_bytes(b"stale content")
 
@@ -461,7 +476,7 @@ class TestEncodeVideoFrames:
         cfg = VideoEncoderConfig(vcodec="libsvtav1", g=4, crf=25, preset=10)
         video_path = _encode_video(tmp_path / "out.mp4", num_frames=4, fps=30, cfg=cfg)
 
-        info = get_video_info(video_path, camera_encoder=cfg)
+        info = get_video_info(video_path, video_encoder=cfg)
 
         # Stream-derived
         assert info["video.height"] == 64
@@ -470,7 +485,7 @@ class TestEncodeVideoFrames:
         assert info["video.codec"] == "av1"
         assert info["video.pix_fmt"] == "yuv420p"
         assert info["video.fps"] == 30
-        assert info["video.is_depth_map"] is False
+        assert info["is_depth_map"] is False
         assert info["has_audio"] is False
         # Encoder config
         assert info["video.g"] == 4
@@ -488,14 +503,14 @@ class TestReencodeVideo:
         src = TEST_ARTIFACTS_DIR / "clip_4frames.mp4"
         out = tmp_path / "reencoded.mp4"
         cfg = VideoEncoderConfig(vcodec="h264", g=6, crf=23, pix_fmt="yuv444p")
-        reencode_video(src, out, camera_encoder=cfg, overwrite=True)
+        reencode_video(src, out, video_encoder=cfg, overwrite=True)
 
         assert out.exists()
         with av.open(str(out)) as container:
             n_frames = sum(1 for _ in container.decode(video=0))
         assert n_frames == 4
 
-        info = get_video_info(out, camera_encoder=cfg)
+        info = get_video_info(out, video_encoder=cfg)
         assert info["video.codec"] == "h264"
         assert info["video.pix_fmt"] == "yuv444p"
         assert info["video.height"] == 64
@@ -509,7 +524,7 @@ class TestReencodeVideo:
         src = TEST_ARTIFACTS_DIR / "clip_6frames.mp4"
         out = tmp_path / "trim_window.mp4"
         cfg = VideoEncoderConfig(vcodec="h264")
-        reencode_video(src, out, camera_encoder=cfg, start_time_s=0.05, end_time_s=0.12, overwrite=True)
+        reencode_video(src, out, video_encoder=cfg, start_time_s=0.05, end_time_s=0.12, overwrite=True)
 
         with av.open(str(out)) as container:
             frames = list(container.decode(video=0))
@@ -580,10 +595,10 @@ class TestEncoderConfigPersistence:
     def test_first_episode_save_persists_encoder_config(self, tmp_path, empty_lerobot_dataset_factory):
         cfg = VideoEncoderConfig(vcodec="libsvtav1", g=2, crf=30, preset=12)
         dataset = empty_lerobot_dataset_factory(
-            root=tmp_path / "ds", features=VIDEO_FEATURES, use_videos=True, camera_encoder=cfg
+            root=tmp_path / "ds", features=DUMMY_VIDEO_FEATURES, use_videos=True, camera_encoder=cfg
         )
 
-        _add_frames(dataset, num_frames=4)
+        add_frames(dataset, num_frames=4)
         dataset.save_episode()
         dataset.finalize()
 
@@ -603,14 +618,14 @@ class TestEncoderConfigPersistence:
     def test_second_episode_does_not_overwrite_encoder_fields(self, tmp_path, empty_lerobot_dataset_factory):
         cfg = VideoEncoderConfig(vcodec="libsvtav1", g=2, crf=30, preset=12)
         dataset = empty_lerobot_dataset_factory(
-            root=tmp_path / "ds", features=VIDEO_FEATURES, use_videos=True, camera_encoder=cfg
+            root=tmp_path / "ds", features=DUMMY_VIDEO_FEATURES, use_videos=True, camera_encoder=cfg
         )
 
-        _add_frames(dataset, num_frames=4)
+        add_frames(dataset, num_frames=4)
         dataset.save_episode()
         first_info = dict(_read_feature_info(dataset))
 
-        _add_frames(dataset, num_frames=4)
+        add_frames(dataset, num_frames=4)
         dataset.save_episode()
         dataset.finalize()
 
@@ -637,3 +652,217 @@ class TestFromVideoInfo:
         # ``{}`` placeholder (typical after a merge with disagreeing sources)
         # must not leak into the reconstructed config.
         assert cfg.extra_options == VideoEncoderConfig().extra_options
+
+
+# ─── Depth-specific encoding tests ────────────────────────────────────
+
+
+class TestEncodeDepthVideoFrames:
+    """Depth mirror of :class:`TestEncodeVideoFrames`.
+
+    Exercises ``encode_video_frames`` end-to-end through
+    :class:`DepthEncoderConfig` (HEVC Main 12 / ``gray12le``) on synthetic
+    uint16 depth TIFFs.
+    """
+
+    @require_hevc
+    def test_produces_readable_file(self, tmp_path):
+        video_path = _encode_video(tmp_path / "out.mp4", depth=True)
+
+        assert video_path.exists()
+        info = get_video_info(video_path, video_encoder=DepthEncoderConfig())
+        assert info["video.height"] == 64
+        assert info["video.width"] == 96
+        assert info["video.codec"] == "hevc"
+        assert info["video.pix_fmt"] == "gray12le"
+        assert info["video.channels"] == 1
+        assert info["is_depth_map"] is True
+
+    @require_hevc
+    def test_frame_count_and_duration_match_input(self, tmp_path):
+        num_frames = 10
+        fps = 30
+        video_path = _encode_video(tmp_path / "out.mp4", num_frames=num_frames, fps=fps, depth=True)
+
+        with av.open(str(video_path)) as container:
+            stream = container.streams.video[0]
+            actual_frames = sum(1 for _ in container.decode(stream))
+            duration = (
+                float(stream.duration * stream.time_base)
+                if stream.duration is not None
+                else float(container.duration / av.time_base)
+            )
+
+        assert actual_frames == num_frames
+        assert abs(duration - num_frames / fps) < 0.1
+
+    def test_overwrite_false_skips_existing_file(self, tmp_path):
+        """Codec-agnostic: file-system semantics must hold even without an HEVC encoder."""
+        imgs_dir = tmp_path / "imgs"
+        _write_depth_frames(imgs_dir)
+        video_path = tmp_path / "out.mp4"
+        sentinel = b"pre-existing depth content"
+        video_path.write_bytes(sentinel)
+
+        encode_video_frames(imgs_dir, video_path, fps=30, video_encoder=DepthEncoderConfig(), overwrite=False)
+
+        assert video_path.read_bytes() == sentinel
+
+    @require_hevc
+    def test_overwrite_true_replaces_existing_file(self, tmp_path):
+        imgs_dir = tmp_path / "imgs"
+        _write_depth_frames(imgs_dir)
+        video_path = tmp_path / "out.mp4"
+        video_path.write_bytes(b"stale content")
+
+        encode_video_frames(imgs_dir, video_path, fps=30, video_encoder=DepthEncoderConfig(), overwrite=True)
+
+        info = get_video_info(video_path, video_encoder=DepthEncoderConfig())
+        assert info["video.height"] == 64
+        assert info["video.pix_fmt"] == "gray12le"
+        assert info["is_depth_map"] is True
+
+    @require_hevc
+    def test_custom_encoder_config_fields_stored_in_info(self, tmp_path):
+        """All stream-derived and depth-encoder config fields are present after encoding."""
+        cfg = DepthEncoderConfig(
+            vcodec="hevc",
+            pix_fmt="gray12le",
+            g=4,
+            crf=25,
+            depth_min=0.05,
+            depth_max=8.0,
+            shift=2.5,
+            use_log=False,
+        )
+        video_path = _encode_video(tmp_path / "out.mp4", num_frames=4, fps=30, cfg=cfg, depth=True)
+
+        info = get_video_info(video_path, video_encoder=cfg)
+
+        # Stream-derived
+        assert info["video.height"] == 64
+        assert info["video.width"] == 96
+        assert info["video.channels"] == 1
+        assert info["video.codec"] == "hevc"
+        assert info["video.pix_fmt"] == "gray12le"
+        assert info["video.fps"] == 30
+        assert info["is_depth_map"] is True
+        assert info["has_audio"] is False
+        # Base encoder config
+        assert info["video.g"] == 4
+        assert info["video.crf"] == 25
+        assert info["video.fast_decode"] == 0
+        assert info["video.video_backend"] == "pyav"
+        assert info["video.extra_options"] == {}
+        # Depth-specific tuning
+        assert info["video.depth_min"] == 0.05
+        assert info["video.depth_max"] == 8.0
+        assert info["video.shift"] == 2.5
+        assert info["video.use_log"] is False
+
+
+class TestDepthEncoderConfigPersistence:
+    """Depth mirror of :class:`TestEncoderConfigPersistence`.
+
+    ``DepthEncoderConfig`` must be stored as ``video.<field>`` entries
+    (including the depth-specific ``depth_min`` / ``depth_max`` / ``shift`` /
+    ``use_log``) under ``info["features"][<depth_key>]["info"]`` when the
+    first episode is saved.
+    """
+
+    @require_hevc
+    def test_first_episode_save_persists_depth_encoder_config(self, tmp_path, empty_lerobot_dataset_factory):
+        cfg = DepthEncoderConfig(
+            vcodec="hevc",
+            pix_fmt="gray12le",
+            g=2,
+            crf=30,
+            depth_min=0.05,
+            depth_max=8.0,
+            shift=2.5,
+            use_log=False,
+        )
+        dataset = empty_lerobot_dataset_factory(
+            root=tmp_path / "ds", features=DUMMY_DEPTH_FEATURES, use_videos=True, depth_encoder=cfg
+        )
+
+        add_frames(dataset, num_frames=4)
+        dataset.save_episode()
+        dataset.finalize()
+
+        info = _read_feature_info(dataset, key=DUMMY_DEPTH_KEY)
+
+        # Stream-derived
+        assert info["video.height"] == 64
+        assert info["video.width"] == 96
+        assert info["video.fps"] == 30
+        assert info["video.codec"] == "hevc"
+        assert info["video.pix_fmt"] == "gray12le"
+        assert info["is_depth_map"] is True
+        # Base encoder config
+        assert info["video.g"] == 2
+        assert info["video.crf"] == 30
+        assert info["video.fast_decode"] == 0
+        assert info["video.video_backend"] == "pyav"
+        assert info["video.extra_options"] == {}
+        # Depth-specific tuning
+        assert info["video.depth_min"] == 0.05
+        assert info["video.depth_max"] == 8.0
+        assert info["video.shift"] == 2.5
+        assert info["video.use_log"] is False
+
+    @require_hevc
+    def test_second_episode_does_not_overwrite_depth_encoder_fields(
+        self, tmp_path, empty_lerobot_dataset_factory
+    ):
+        cfg = DepthEncoderConfig(
+            vcodec="hevc",
+            pix_fmt="gray12le",
+            g=2,
+            crf=30,
+            depth_min=0.05,
+            depth_max=8.0,
+            shift=2.5,
+            use_log=False,
+        )
+        dataset = empty_lerobot_dataset_factory(
+            root=tmp_path / "ds", features=DUMMY_DEPTH_FEATURES, use_videos=True, depth_encoder=cfg
+        )
+
+        add_frames(dataset, num_frames=4)
+        dataset.save_episode()
+        first_info = dict(_read_feature_info(dataset, key=DUMMY_DEPTH_KEY))
+
+        add_frames(dataset, num_frames=4)
+        dataset.save_episode()
+        dataset.finalize()
+
+        assert _read_feature_info(dataset, key=DUMMY_DEPTH_KEY) == first_info
+
+
+class TestDepthFromVideoInfo:
+    """``DepthEncoderConfig.from_video_info`` reconstructs a depth encoder
+    config from the ``video.*`` keys persisted in a dataset's ``info.json``.
+
+    Depth mirror of :class:`TestFromVideoInfo`.
+    """
+
+    @require_hevc
+    def test_reconstructs_from_dummy_depth_video_info(self):
+        cfg = DepthEncoderConfig.from_video_info(DUMMY_DEPTH_VIDEO_INFO_FULL)
+
+        # No alias for ``"hevc"``; the canonical stream codec is reused as-is.
+        assert cfg.vcodec == "hevc"
+        assert cfg.pix_fmt == DUMMY_DEPTH_VIDEO_INFO_FULL["video.pix_fmt"]
+        assert cfg.g == DUMMY_DEPTH_VIDEO_INFO_FULL["video.g"]
+        assert cfg.crf == DUMMY_DEPTH_VIDEO_INFO_FULL["video.crf"]
+        assert cfg.fast_decode == DUMMY_DEPTH_VIDEO_INFO_FULL["video.fast_decode"]
+        assert cfg.video_backend == DUMMY_DEPTH_VIDEO_INFO_FULL["video.video_backend"]
+        # ``{}`` placeholder (typical after a merge with disagreeing sources)
+        # must not leak into the reconstructed config.
+        assert cfg.extra_options == DepthEncoderConfig().extra_options
+        # Depth-specific tuning round-trips through ``info.json``.
+        assert cfg.depth_min == DUMMY_DEPTH_VIDEO_INFO_FULL["video.depth_min"]
+        assert cfg.depth_max == DUMMY_DEPTH_VIDEO_INFO_FULL["video.depth_max"]
+        assert cfg.shift == DUMMY_DEPTH_VIDEO_INFO_FULL["video.shift"]
+        assert cfg.use_log == DUMMY_DEPTH_VIDEO_INFO_FULL["video.use_log"]

tests/fixtures/constants.py

@@ -39,12 +39,56 @@ DUMMY_VIDEO_INFO = {
     "video.crf": 30,
     "video.preset": 12,
     "video.fast_decode": 0,
-    "video.is_depth_map": False,
+    "is_depth_map": False,
     "has_audio": False,
 }
 DUMMY_CAMERA_FEATURES = {
     "laptop": {"shape": (64, 96, 3), "names": ["height", "width", "channels"], "info": DUMMY_VIDEO_INFO},
     "phone": {"shape": (64, 96, 3), "names": ["height", "width", "channels"], "info": DUMMY_VIDEO_INFO},
 }
+DUMMY_DEPTH_VIDEO_INFO = {
+    **DUMMY_VIDEO_INFO,
+    "is_depth_map": True,
+}
+DUMMY_DEPTH_VIDEO_INFO_FULL = {
+    **{k: v for k, v in DUMMY_VIDEO_INFO.items() if k != "video.preset"},
+    "video.codec": "hevc",
+    "video.pix_fmt": "gray12le",
+    "is_depth_map": True,
+    "video.depth_min": 0.05,
+    "video.depth_max": 8.0,
+    "video.shift": 2.5,
+    "video.use_log": True,
+}
+DUMMY_DEPTH_CAMERA_FEATURES = {
+    "laptop_depth": {
+        "shape": (64, 96, 1),
+        "names": ["height", "width", "channels"],
+        "info": DUMMY_DEPTH_VIDEO_INFO,
+    },
+}
+DUMMY_CAMERA_FEATURES_WITH_DEPTH = {**DUMMY_CAMERA_FEATURES, **DUMMY_DEPTH_CAMERA_FEATURES}
 DUMMY_CHW = (3, 96, 128)
 DUMMY_HWC = (96, 128, 3)
+
+# Default video feature set used by video-encoding persistence tests.
+DUMMY_VIDEO_FEATURES = {
+    "observation.images.cam": {
+        "dtype": "video",
+        "shape": (64, 96, 3),
+        "names": ["height", "width", "channels"],
+    },
+    "action": {"dtype": "float32", "shape": (2,), "names": ["a", "b"]},
+}
+DUMMY_VIDEO_KEY = "observation.images.cam"
+
+DUMMY_DEPTH_FEATURES = {
+    "observation.images.depth": {
+        "dtype": "video",
+        "shape": (64, 96, 1),
+        "names": ["height", "width", "channels"],
+        "info": {"is_depth_map": True},
+    },
+    "action": {"dtype": "float32", "shape": (2,), "names": ["a", "b"]},
+}
+DUMMY_DEPTH_KEY = "observation.images.depth"

tests/fixtures/dataset_factories.py

@@ -49,6 +49,39 @@ from tests.fixtures.constants import (
 )
 
 
+def add_frames(dataset: LeRobotDataset, num_frames: int) -> None:
+    """Append ``num_frames`` synthetic frames to ``dataset``.
+
+    Generates per-feature payloads from ``dataset.meta``: uint16 depth ramps for
+    keys in ``dataset.meta.depth_keys``, uint8 random noise for video/image keys,
+    and float32 zeros for everything else. ``DEFAULT_FEATURES`` (timestamp,
+    frame_index, ...) are auto-populated by ``add_frame`` and skipped here.
+    """
+    video_keys = dataset.meta.video_keys
+    depth_keys = dataset.meta.depth_keys
+    # Smooth gradient base reused per (H, W) to keep depth frames cheap to
+    # encode (HEVC Main 12 hates white noise).
+    _depth_base_cache: dict[tuple[int, int], np.ndarray] = {}
+    for i in range(num_frames):
+        frame: dict = {"task": "test"}
+        for key, ft in dataset.meta.features.items():
+            if key in DEFAULT_FEATURES:
+                continue
+            shape = ft["shape"]
+            if key in depth_keys:
+                h, w, _ = shape
+                base = _depth_base_cache.setdefault(
+                    (h, w),
+                    np.linspace(100.0, 10_000.0, h * w, dtype=np.float32).reshape(h, w, 1),
+                )
+                frame[key] = (base + 50.0 * i).clip(0, 65535).astype(np.uint16)
+            elif key in video_keys:
+                frame[key] = np.random.randint(0, 256, shape, dtype=np.uint8)
+            else:
+                frame[key] = np.zeros(shape, dtype=np.float32)
+        dataset.add_frame(frame)
+
+
 class LeRobotDatasetFactory(Protocol):
     def __call__(self, *args, **kwargs) -> LeRobotDataset: ...
 
@@ -485,10 +518,14 @@ def lerobot_dataset_factory(
         hf_dataset: datasets.Dataset | None = None,
         data_files_size_in_mb: float = DEFAULT_DATA_FILE_SIZE_IN_MB,
         chunks_size: int = DEFAULT_CHUNK_SIZE,
+        camera_features: dict | None = None,
         **kwargs,
     ) -> LeRobotDataset:
         # Instantiate objects
         if info is None:
+            info_kwargs = {}
+            if camera_features is not None:
+                info_kwargs["camera_features"] = camera_features
             info = info_factory(
                 total_episodes=total_episodes,
                 total_frames=total_frames,
@@ -496,6 +533,7 @@ def lerobot_dataset_factory(
                 use_videos=use_videos,
                 data_files_size_in_mb=data_files_size_in_mb,
                 chunks_size=chunks_size,
+                **info_kwargs,
             )
         if stats is None:
             stats = stats_factory(features=info.features)

tests/processor/test_pipeline.py

@@ -2370,14 +2370,32 @@ def test_aggregate_images_when_use_videos_false():
     out = aggregate_pipeline_dataset_features(
         pipeline=rp,
         initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
-        use_videos=False,  # expect "image" dtype
+        use_videos=False,  # images kept, stored as "image" dtype
         patterns=None,
     )
 
     key = f"{OBS_IMAGES}.back"
     key_front = f"{OBS_IMAGES}.front"
-    assert key not in out
+    assert key in out
-    assert key_front not in out
+    assert key_front in out
+    assert out[key]["dtype"] == "image"
+    assert out[key_front]["dtype"] == "image"
+    assert out[key]["shape"] == initial["back"]
+
+
+def test_aggregate_images_excluded():
+    rp = DataProcessorPipeline([AddObservationStateFeatures(add_front_image=True)])
+    initial = {"back": (480, 640, 3)}
+
+    out = aggregate_pipeline_dataset_features(
+        pipeline=rp,
+        initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
+        exclude_images=True,
+        patterns=None,
+    )
+
+    assert f"{OBS_IMAGES}.back" not in out
+    assert f"{OBS_IMAGES}.front" not in out
 
 
 def test_aggregate_images_when_use_videos_true():

tests/scripts/test_edit_dataset_parsing.py

@@ -27,6 +27,7 @@ from lerobot.scripts.lerobot_edit_dataset import (
     MergeConfig,
     ModifyTasksConfig,
     OperationConfig,
+    ReencodeVideosConfig,
     RemoveFeatureConfig,
     SplitConfig,
     _validate_config,
@@ -103,3 +104,47 @@ class TestOperationTypeParsing:
         )
         resolved_name = OperationConfig.get_choice_name(type(cfg.operation))
         assert resolved_name == type_name
+
+
+class TestDepthEncoderParsing:
+    """Test that the depth encoder is exposed and parsed for video operations."""
+
+    def test_reencode_has_default_depth_encoder(self):
+        cfg = parse_cfg(["--repo_id", "test/repo", "--operation.type", "reencode_videos"])
+        assert isinstance(cfg.operation, ReencodeVideosConfig)
+        # A depth encoder is configured by default so depth videos are re-encoded too.
+        assert cfg.operation.depth_encoder is not None
+        assert hasattr(cfg.operation.depth_encoder, "depth_min")
+
+    def test_reencode_parses_depth_encoder_overrides(self):
+        cfg = parse_cfg(
+            [
+                "--repo_id",
+                "test/repo",
+                "--operation.type",
+                "reencode_videos",
+                "--operation.depth_encoder.vcodec",
+                "ffv1",
+                "--operation.depth_encoder.depth_max",
+                "12.0",
+                "--operation.depth_encoder.use_log",
+                "false",
+            ]
+        )
+        assert cfg.operation.depth_encoder.vcodec == "ffv1"
+        assert cfg.operation.depth_encoder.depth_max == 12.0
+        assert cfg.operation.depth_encoder.use_log is False
+
+    def test_convert_image_to_video_parses_depth_encoder_overrides(self):
+        cfg = parse_cfg(
+            [
+                "--repo_id",
+                "test/repo",
+                "--operation.type",
+                "convert_image_to_video",
+                "--operation.depth_encoder.depth_min",
+                "0.05",
+            ]
+        )
+        assert isinstance(cfg.operation, ConvertImageToVideoConfig)
+        assert cfg.operation.depth_encoder.depth_min == 0.05

tests/teleoperators/test_rebot_102_leader.py

@@ -18,7 +18,7 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
-from lerobot.teleoperators.bi_rebot_102_leader import BiRebotArm102Leader, BiRebotArm102LeaderConfig
+from lerobot.teleoperators.bi_rebot_102_leader import BiRebot102Leader, BiRebot102LeaderConfig
 from lerobot.teleoperators.rebot_102_leader import (
     RebotArm102Leader,
     RebotArm102LeaderConfig,
@@ -91,11 +91,11 @@ def test_send_feedback_not_implemented(leader):
 
 def test_bimanual_prefixes_features():
     with patch(f"{_MODULE}.require_package", lambda *a, **kw: None):
-        cfg = BiRebotArm102LeaderConfig(
+        cfg = BiRebot102LeaderConfig(
             left_arm_config=RebotArm102LeaderConfig(port="/dev/null0"),
             right_arm_config=RebotArm102LeaderConfig(port="/dev/null1"),
         )
-        teleop = BiRebotArm102Leader(cfg)
+        teleop = BiRebot102Leader(cfg)
     assert any(k.startswith("left_") for k in teleop.action_features)
     assert any(k.startswith("right_") for k in teleop.action_features)
     assert "left_gripper.pos" in teleop.action_features

tests/utils/test_visualization_utils.py

@@ -43,6 +43,11 @@ def mock_rerun(monkeypatch):
         def __init__(self, arr):
             self.arr = arr
 
+    class DummyDepthImage:
+        def __init__(self, arr, colormap=None):
+            self.arr = arr
+            self.colormap = colormap
+
     def dummy_log(key, obj=None, **kwargs):
         # Accept either positional `obj` or keyword `entity` and record remaining kwargs.
         if obj is None and "entity" in kwargs:
@@ -55,6 +60,8 @@ def mock_rerun(monkeypatch):
         __spec__=SimpleNamespace(name="rerun", submodule_search_locations=None),
         Scalars=DummyScalar,
         Image=DummyImage,
+        DepthImage=DummyDepthImage,
+        components=SimpleNamespace(Colormap=SimpleNamespace(Viridis="viridis")),
         log=dummy_log,
         init=lambda *a, **k: None,
         spawn=lambda *a, **k: None,
@@ -225,7 +232,7 @@ def test_log_rerun_data_kwargs_only(mock_rerun):
     assert temp.value == pytest.approx(10.0)
 
     img = _obj_for(calls, "observation.gray")
-    assert type(img).__name__ == "DummyImage"
+    assert type(img).__name__ == "DummyDepthImage"  # single-channel -> DepthImage
     assert img.arr.shape == (8, 8, 1)  # remains HWC
     assert _kwargs_for(calls, "observation.gray").get("static", False) is True