feat(depth maps): adding support for depth in LeRobot (#3644)

* feat(depth): add depth quantization helpers and tests

* feat(video): add ffv1 to supported codecs

* feat(depth): persist depth metadata

* feat(depth): extend quantization tools to better fit the encoding/decoding pipeline

* feat(depth): plumb DepthEncoderConfig through LeRobotDataset and DatasetWriter

* feat(depth): wire StreamingVideoEncoder + writer to depth encoder

* feat(depth): wire DatasetReader to decode_depth_frames

* feat(cameras/realsense): expose async depth in metric meters

* feat(features): route 2D camera shapes to observation.depth.<key>

* feat(robots/so_follower): emit + populate depth keys when use_depth

* feat(record): plumb DepthEncoderConfig through lerobot-record

* feat(viz): render depth observations as rr.DepthImage in Viridis

* feat(depth maps writer): adding support for raw depth maps recording with image writer

* chore(format): format code

* feat(depth shape): ensuring depth maps shape is always including the channel

* feat(is_depth): simplifying is_depth nested name + legacy support

* fix(stop_event): fixing stop_event race condition in camera classes

* fix(plumbing): fixing missing parts in the depth maps pipeline

* chore(typos): fixing typos

* test(fix): fixing exisiting tests to still work with latest features

* tests(depth): adding new tests for depth integration validation

* feat(pix_fmt channels): use PyAv to check get pixel formats number of channels

* feat(refactor): refactor DepthEncoderConfig quantization pipeline, so that the methods do not live in the config class. Add pixel format - channels validation.Move the default pixel format for depth in the config file.

* fix(pre-commit): fixing mutable defautl value

* fix(info): fixing info metadata update when is_depth_map was set

* tests(typos): fixing typos in tests

* fix(realsense): fixing typo in realsense serial number

* fix(normalization): restricting 255 normalization to non depth/uint8 images only

* fix(typo): fixing typo

* fix(TIFF): add missing quantization and cleanup for TIFF files

* feat(batched dequantization): optimizing dequantize_depth for torch based batched dequantization

* feat(tools): adding depth support in LeRobotDataset edition tools

* test(aggregate): extending aggregation tests to depth frames

* test(cleaning): cleaning up tests

* fix(from_video_info): fixing early validation issue in from_video_info

* fix(typo): fixing typo

* fix(is_depth): adding missing doctrings and is_depth arguments in video decoding functions

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

* fix(depth units): fixing depth units output for the realsense cameras

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

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

* test(depth): cleaning up depth tests

* test(depth encoding): updating and cleaning video/depth encoding tests

* chore(format): formatting code

* docs(depth): improving depth maps docs

* test(fix): fixing depth tests

* test(dataset tools): adding missing tests for new dataset edition tools features

* chore(format): formatting code

* fix(pyav check): fixing PyAV option validation for integer codec options by normalizing
numeric values before calling `is_integer()`

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

* docs(mermaid): fixing mermaid diagram

* fix(rebase): rebase follow up corrections

* feat(dataset tools): adding missing docstrings and features for depth fill support in dataset edition tools

* docs(docstring): updating docstrings

* docs(dataset tools): updating docs

* fix(save images): fixing image saving in dataset tools

* fix(update video info): fixing update video info logic to match the recording and editing use cases

* test(reencode): fixing reencoding monkeypatch

* fix(review): add Claude review

* chore(format): format code

* fix(update video info): ditching the differentiated approahces for video info update - video info are always updated unless for preserved keys.

* chore(rebase): fixing rebase merge conflicts

* test(visualization): fixing visualization tests

* feat(docstrings): adding explicit docstring for encoding parameters. Docstrigns will now show up as description in the CLI --help.

* feat(mm as default): adding a global DEFAULT_DEPTH_UNIT variable setting mm as default depth unit

* fix(RGB <-> camera): renaming camera_encoder to rgb_encoder for clarity

* chore(TODO): removing deprecated TODO

* doc(write_u16_plane): improving docstrings for write_u16_plane

* feat(units): adding constants for depth frames units (m and mm)

* fix(spam): replacing spamming warning but a debug log

* feat(leagcy metadata): adding automatic metadata update for legacy 'video.is_depth_map' feature

* fix(copy&reindex): fixing metadat reshaping for single channel frames

* fix(ImageNet): excluding dpeth frames from ImageNet stats

* fix(PyAV container seek): fixing initial  PyAV container seek to be robust againsy codec choice

* feat(lerobot-dataset-viz): adding support for depth in lerobot-dataset-viz

* fix(compress): removing rerun compression for DepthImages

* fix(signle channel squeeze): fixing single channel squeezing

* chore(format): format code

* fix(streaming): adding support for dequantization in streaming_dataset.py

* refactor(read depth): factorizing depth reading methods for realsense camera and adding support for depth-only usage

* chore(renaming): fixing missed RGBEncoderConfig renamings

* docs(renaming): reflecting renamings in a clearer way in the docs

* chore(annotation): excluding depth from the annotation pipeline

* feat(robots): adding depth support in compatible follower robots

* feat(LeSadKiwi): excluding LeKiwi from depth support (for now)

* chore(fail): removing misplaced file

* chore(fail): removing misplaced file

* fix(remove ffv1): removing ffv1 as it does not support MP4

* docs(cheat sheet): adding depth and video encoding to the cheat sheet

* fix(lossless): tuning depth encoding parameters for lossless depth storage

* test(fix): fixing failing tests

* depth(ZMQ): excluding ZMQ from depth support

* Revert "depth(ZMQ): excluding ZMQ from depth support"

This reverts commit b95cf4e4c2.

* fix(image transforms): excluding depth frames from images transforms

* fix(typo): typo

* fix(stats): fixing stats computation for depth frames

* fix(TIFF vs. pytorch): adding an extra uint16 to float32 conversion for depth maps stored as raw TIFF images

* fix(typos): fixing typos

* test(dtype): fixing stats computation typing tests

---------

Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi Ai <wsai@stanford.edu>

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/cheat-sheet.mdx

@@ -89,6 +89,36 @@ Control the data recording flow using keyboard shortcuts:
 - Press **Left Arrow (`←`)**: Delete current episode and retry.
 - Press **Escape (`ESC`)**: Stop, encode videos, and upload.
 
+### Recording depth
+
+Intel RealSense cameras (`type: intelrealsense`) record a depth stream when you set `use_depth: true`. Depth is quantized to 12-bit codes and stored as its own video.
+
+```bash
+lerobot-record \
+    ... \
+    --robot.cameras="{ head: {type: intelrealsense, serial_number_or_name: \"0123456789\", width: 640, height: 480, fps: 30, use_depth: true} }" \
+    --dataset.repo_id=${HF_USER}/so101_depth_test \
+    --dataset.single_task="put the red brick in a bowl" \
+    --dataset.depth_encoder.depth_min=0.01 \
+    --dataset.depth_encoder.depth_max=10.0 \
+    --dataset.depth_encoder.shift=0.0 \
+    --dataset.depth_encoder.use_log=true
+```
+
+### Video encoding parameters
+
+RGB and depth streams are encoded independently via the `--dataset.rgb_encoder.*` and `--dataset.depth_encoder.*` keys.
+
+```bash
+lerobot-record \
+    ... \
+    --dataset.rgb_encoder.vcodec=h264 \
+    --dataset.rgb_encoder.pix_fmt=yuv420p \
+    --dataset.rgb_encoder.crf=23 \
+    --dataset.depth_encoder.vcodec=hevc \
+    --dataset.depth_encoder.extra_options='{"x265-params": "lossless=1"}'
+```
+
 ### Training
 
 Depending on your hardware training the policy might take a few hours. That's how you train simple `ACT` policy:

docs/source/earthrover_mini_plus.mdx

@@ -194,7 +194,7 @@ lerobot-record \
     --dataset.single_task="Navigate around obstacles" \
     --dataset.streaming_encoding=true \
     --dataset.encoder_threads=2 \
-    # --dataset.camera_encoder.vcodec=auto \
+    # --dataset.rgb_encoder.vcodec=auto \
     --display_data=true
 ```
 

docs/source/groot.mdx

@@ -124,7 +124,7 @@ lerobot-rollout\
   --dataset.single_task="Grab and handover the red cube to the other arm" \
   --dataset.streaming_encoding=true \
   --dataset.encoder_threads=2 \
-  # --dataset.camera_encoder.vcodec=auto \
+  # --dataset.rgb_encoder.vcodec=auto \
   --policy.path=<user>/groot-bimanual \ # your trained model
   --duration=600
 ```

docs/source/hope_jr.mdx

@@ -232,7 +232,7 @@ lerobot-record \
     --dataset.private=true \
     --dataset.streaming_encoding=true \
     --dataset.encoder_threads=2 \
-    # --dataset.camera_encoder.vcodec=auto \
+    # --dataset.rgb_encoder.vcodec=auto \
     --display_data=true
 ```
 
@@ -278,6 +278,6 @@ lerobot-record \
   --dataset.num_episodes=10 \
   --dataset.streaming_encoding=true \
   --dataset.encoder_threads=2 \
-  # --dataset.camera_encoder.vcodec=auto \
+  # --dataset.rgb_encoder.vcodec=auto \
   --policy.path=outputs/train/hopejr_hand/checkpoints/last/pretrained_model
 ```

docs/source/il_robots.mdx

@@ -207,7 +207,7 @@ lerobot-record \
     --dataset.num_episodes=5 \
     --dataset.single_task="Grab the black cube" \
     --dataset.streaming_encoding=true \
-    # --dataset.camera_encoder.vcodec=auto \
+    # --dataset.rgb_encoder.vcodec=auto \
     --dataset.encoder_threads=2
 ```
 </hfoption>

docs/source/lerobot-dataset-v3.mdx

@@ -44,7 +44,7 @@ lerobot-record \
   --dataset.num_episodes=5 \
   --dataset.single_task="Grab the black cube" \
   --dataset.streaming_encoding=true \
-  # --dataset.camera_encoder.vcodec=auto \
+  # --dataset.rgb_encoder.vcodec=auto \
   --dataset.encoder_threads=2
 ```
 

docs/source/reachy2.mdx

@@ -161,7 +161,7 @@ lerobot-record \
     --dataset.private=true \
     --dataset.streaming_encoding=true \
     --dataset.encoder_threads=2 \
-    # --dataset.camera_encoder.vcodec=auto \
+    # --dataset.rgb_encoder.vcodec=auto \
     --display_data=true
 ```
 
@@ -203,7 +203,7 @@ lerobot-record \
     --dataset.private=true \
     --dataset.streaming_encoding=true \
     --dataset.encoder_threads=2 \
-    # --dataset.camera_encoder.vcodec=auto \
+    # --dataset.rgb_encoder.vcodec=auto \
     --display_data=true
 ```
 

docs/source/streaming_video_encoding.mdx

@@ -17,7 +17,7 @@ This makes `save_episode()` near-instant (the video is already encoded by the ti
 | Parameter               | CLI Flag                          | Type          | Default       | Description                                                       |
 | ----------------------- | --------------------------------- | ------------- | ------------- | ----------------------------------------------------------------- |
 | `streaming_encoding`    | `--dataset.streaming_encoding`    | `bool`        | `True`        | Enable real-time encoding during capture                          |
-| `vcodec`                | `--dataset.camera_encoder.vcodec` | `str`         | `"libsvtav1"` | Video codec. `"auto"` detects best HW encoder                     |
+| `vcodec`                | `--dataset.rgb_encoder.vcodec`    | `str`         | `"libsvtav1"` | Video codec. `"auto"` detects best HW encoder                     |
 | `encoder_threads`       | `--dataset.encoder_threads`       | `int \| None` | `None` (auto) | Threads per encoder instance. `None` will leave the vcoded decide |
 | `encoder_queue_maxsize` | `--dataset.encoder_queue_maxsize` | `int`         | `30`          | Max buffered frames per camera (~1s at 30fps). Consumes RAM       |
 
@@ -82,15 +82,15 @@ Use HW encoding when:
 
 ### Available HW Encoders
 
-| Encoder             | Platform      | Hardware                                                                                         | CLI Value                                           |
+| Encoder             | Platform      | Hardware                                                                                         | CLI Value                                        |
-| ------------------- | ------------- | ------------------------------------------------------------------------------------------------ | --------------------------------------------------- |
+| ------------------- | ------------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------ |
-| `h264_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.camera_encoder.vcodec=h264_videotoolbox` |
+| `h264_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.rgb_encoder.vcodec=h264_videotoolbox` |
-| `hevc_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.camera_encoder.vcodec=hevc_videotoolbox` |
+| `hevc_videotoolbox` | macOS         | Apple Silicon / Intel                                                                            | `--dataset.rgb_encoder.vcodec=hevc_videotoolbox` |
-| `h264_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.camera_encoder.vcodec=h264_nvenc`        |
+| `h264_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.rgb_encoder.vcodec=h264_nvenc`        |
-| `hevc_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.camera_encoder.vcodec=hevc_nvenc`        |
+| `hevc_nvenc`        | Linux/Windows | NVIDIA GPU                                                                                       | `--dataset.rgb_encoder.vcodec=hevc_nvenc`        |
-| `h264_vaapi`        | Linux         | Intel/AMD GPU                                                                                    | `--dataset.camera_encoder.vcodec=h264_vaapi`        |
+| `h264_vaapi`        | Linux         | Intel/AMD GPU                                                                                    | `--dataset.rgb_encoder.vcodec=h264_vaapi`        |
-| `h264_qsv`          | Linux/Windows | Intel Quick Sync                                                                                 | `--dataset.camera_encoder.vcodec=h264_qsv`          |
+| `h264_qsv`          | Linux/Windows | Intel Quick Sync                                                                                 | `--dataset.rgb_encoder.vcodec=h264_qsv`          |
-| `auto`              | Any           | Probes the system for available HW encoders. Falls back to `libsvtav1` if no HW encoder is found | `--dataset.camera_encoder.vcodec=auto`              |
+| `auto`              | Any           | Probes the system for available HW encoders. Falls back to `libsvtav1` if no HW encoder is found | `--dataset.rgb_encoder.vcodec=auto`              |
 
 > [!NOTE]
 > In order to use the HW accelerated encoders you might need to upgrade your GPU drivers.
@@ -100,15 +100,15 @@ Use HW encoding when:
 
 ## 5. Troubleshooting
 
-| Symptom                                                            | Likely Cause                                 | Fix                                                                                                                                                                                                                                                                                                 |
+| Symptom                                                            | Likely Cause                                 | Fix                                                                                                                                                                                                                                                                                              |
-| ------------------------------------------------------------------ | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------------------------------------------------------ | -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| System freezes or choppy robot movement or Rerun visualization lag | CPU starved (100% load usage)                | Close other apps, reduce encoding throughput, lower `encoder_threads`, use `h264`, use `display_data=False`. If the CPU continues to be at 100% then it might be insufficient for your setup, consider `--dataset.streaming_encoding=false` or HW encoding (`--dataset.camera_encoder.vcodec=auto`) |
+| System freezes or choppy robot movement or Rerun visualization lag | CPU starved (100% load usage)                | Close other apps, reduce encoding throughput, lower `encoder_threads`, use `h264`, use `display_data=False`. If the CPU continues to be at 100% then it might be insufficient for your setup, consider `--dataset.streaming_encoding=false` or HW encoding (`--dataset.rgb_encoder.vcodec=auto`) |
-| "Encoder queue full" warnings or dropped frames in dataset         | Encoder can't keep up (Queue overflow)       | If CPU is not at 100%: Increase `encoder_threads`, increase `encoder_queue_maxsize` or use HW encoding (`--dataset.camera_encoder.vcodec=auto`).                                                                                                                                                    |
+| "Encoder queue full" warnings or dropped frames in dataset         | Encoder can't keep up (Queue overflow)       | If CPU is not at 100%: Increase `encoder_threads`, increase `encoder_queue_maxsize` or use HW encoding (`--dataset.rgb_encoder.vcodec=auto`).                                                                                                                                                    |
-| High RAM usage                                                     | Queue filling faster than encoding           | `encoder_threads` too low or CPU insufficient. Reduce `encoder_queue_maxsize` or use HW encoding                                                                                                                                                                                                    |
+| High RAM usage                                                     | Queue filling faster than encoding           | `encoder_threads` too low or CPU insufficient. Reduce `encoder_queue_maxsize` or use HW encoding                                                                                                                                                                                                 |
-| Large video files                                                  | Using HW encoder or H.264                    | Expected trade-off. Switch to `libsvtav1` if CPU allows                                                                                                                                                                                                                                             |
+| Large video files                                                  | Using HW encoder or H.264                    | Expected trade-off. Switch to `libsvtav1` if CPU allows                                                                                                                                                                                                                                          |
-| `save_episode()` still slow                                        | `streaming_encoding` is `False`              | Set `--dataset.streaming_encoding=true`                                                                                                                                                                                                                                                             |
+| `save_episode()` still slow                                        | `streaming_encoding` is `False`              | Set `--dataset.streaming_encoding=true`                                                                                                                                                                                                                                                          |
-| Encoder thread crash                                               | Codec not available or invalid settings      | Check `vcodec` is installed, try `--dataset.camera_encoder.vcodec=auto`                                                                                                                                                                                                                             |
+| Encoder thread crash                                               | Codec not available or invalid settings      | Check `vcodec` is installed, try `--dataset.rgb_encoder.vcodec=auto`                                                                                                                                                                                                                             |
-| Recorded dataset is missing frames                                 | CPU/GPU starvation or occasional load spikes | If ~5% of frames are missing, your system is likely overloaded — follow the recommendations above. If fewer frames are missing (~2%), they are probably due to occasional transient load spikes (often at startup) and can be considered expected.                                                  |
+| Recorded dataset is missing frames                                 | CPU/GPU starvation or occasional load spikes | If ~5% of frames are missing, your system is likely overloaded — follow the recommendations above. If fewer frames are missing (~2%), they are probably due to occasional transient load spikes (often at startup) and can be considered expected.                                               |
 
 ## 6. Recommended Configurations
 
@@ -146,7 +146,7 @@ On very constrained systems, streaming encoding may compete too heavily with the
 # 2camsx 640x480x3 @30fps: Requires some tuning.
 
 # Use H.264, disable streaming, consider batching encoding
-lerobot-record --dataset.camera_encoder.vcodec=h264 --dataset.streaming_encoding=false ...
+lerobot-record --dataset.rgb_encoder.vcodec=h264 --dataset.streaming_encoding=false ...
 ```
 
 ## 7. Closing note

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`.
@@ -117,10 +118,19 @@ lerobot-edit-dataset \
     --repo_id lerobot/pusht_image \
     --operation.type convert_image_to_video \
     --operation.output_dir outputs/pusht_video \
-    --operation.camera_encoder.vcodec libsvtav1 \
+    --operation.rgb_encoder.vcodec libsvtav1 \
-    --operation.camera_encoder.pix_fmt yuv420p \
+    --operation.rgb_encoder.pix_fmt yuv420p \
-    --operation.camera_encoder.g 2 \
+    --operation.rgb_encoder.g 2 \
-    --operation.camera_encoder.crf 30
+    --operation.rgb_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 \
@@ -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.
+- `rgb_encoder`: Video encoder settings applied to RGB cameras — all sub-fields accessible via `--operation.rgb_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 `rgb_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 `rgb_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.rgb_encoder.vcodec h264 \
+    --operation.rgb_encoder.pix_fmt yuv420p \
+    --operation.rgb_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.rgb_encoder.vcodec h264 \
+    --operation.depth_encoder.crf 50
+```
+
+**Parameters:**
+
+- `rgb_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

@@ -2,15 +2,15 @@
 
 When video storage is enabled, LeRobot stores each camera stream as an **MP4** file instead of saving one image file per timestep. Video encoding compresses across time, which usually cuts dataset size and I/O compared to a pile of PNG, while keeping MP4 — a format every player and loader understands.
 
-Encoding frames into an MP4 is a full FFmpeg pipeline: choice of encoder, pixel format, GOP/keyframes, quality vs. speed, and optional extra encoder flags. Most of these knobs are user-tunable through `camera_encoder`, a nested `VideoEncoderConfig` (`lerobot.configs.video.VideoEncoderConfig`) passed through PyAV.
+Encoding frames into an MP4 is a full FFmpeg pipeline: choice of encoder, pixel format, GOP/keyframes, quality vs. speed, and optional extra encoder flags. Most of these knobs are user-tunable through `rgb_encoder`, a nested `RGBEncoderConfig` (`lerobot.configs.video.RGBEncoderConfig`) passed through PyAV.
 
-You can set these parameters from the CLI with `--dataset.camera_encoder.<field>` (e.g. with `lerobot-record` or `lerobot-rollout`). The same block applies to every camera video stream in that run.
+You can set these parameters from the CLI with `--dataset.rgb_encoder.<field>` (e.g. with `lerobot-record` or `lerobot-rollout`). The same block applies to every camera video stream in that run.
 
 <Tip>
-  Video storage must be on for `camera_encoder` to have any effect —
+  Video storage must be on for `rgb_encoder` to have any effect —
   `use_videos=True` in Python APIs, or `--dataset.video=true` on the CLI (the
-  recording default). With video off, inputs stay as images and `camera_encoder`
+  recording default). With video off, inputs stay as images and `rgb_encoder` is
-  is ignored.
+  ignored.
 </Tip>
 
 For details on **when** frames are written vs. encoded (streaming vs. post-episode), queues, and other top-level `--dataset.*` switches, see [Streaming Video Encoding](./streaming_video_encoding). For an encoding-parameter comparison and experiments, see the [video-benchmark Space](https://huggingface.co/spaces/lerobot/video-benchmark).
@@ -33,9 +33,9 @@ lerobot-record \
     --dataset.single_task="Grab the cube" \
     --dataset.streaming_encoding=true \
     --dataset.encoder_threads=2 \
-    --dataset.camera_encoder.vcodec=h264 \
+    --dataset.rgb_encoder.vcodec=h264 \
-    --dataset.camera_encoder.preset=fast \
+    --dataset.rgb_encoder.preset=fast \
-    --dataset.camera_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2} \
+    --dataset.rgb_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2} \
     --display_data=true
 ```
 
@@ -50,7 +50,7 @@ Only override these parameters if you have a specific reason to, and measure the
 
 </Tip>
 
-All flags below are prefixed with `--dataset.camera_encoder.` on the CLI.
+All flags below are prefixed with `--dataset.rgb_encoder.` on the CLI.
 
 | Parameter       | Type             | Default       | Description                                                                                                                                                                            |
 | --------------- | ---------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -65,6 +65,77 @@ 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 shares every `RGBEncoderConfig` 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"`                        | HEVC Main 12 (a 12-bit-capable codec, MP4-compatible).                                                                                 |
+| `extra_options` | `dict`  | `{"x265-params": "lossless=1"}` | **Depth defaults to lossless** (exact round-trip); `crf` is ignored. Pass `extra_options={}` and set `crf` for a smaller lossy stream. |
+| `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 +153,7 @@ 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",
@@ -97,12 +168,12 @@ 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`.
+- **Encoder-derived** (taken from `RGBEncoderConfig` or `DepthEncoderConfig`): `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.video_backend`, `video.extra_options`.
 
 <Tip>
   This block is populated **once**, from the **first** episode. It assumes every
-  episode in the dataset was encoded with the same `camera_encoder`. Changing
+  episode in the dataset was encoded with the same `rgb_encoder`. Changing
   encoder settings partway through a recording is not supported — the
   `info.json` will only reflect the parameters used for the first episode.
 </Tip>

src/lerobot/annotations/steerable_pipeline/frames.py

@@ -36,7 +36,7 @@ from typing import Any, Protocol
 import PIL.Image
 import torch
 
-from lerobot.configs.video import VideoEncoderConfig
+from lerobot.configs import RGBEncoderConfig
 from lerobot.datasets.video_utils import decode_video_frames, reencode_video
 
 from .reader import EpisodeRecord, snap_to_frame
@@ -164,7 +164,9 @@ class VideoFrameProvider:
         # only for video-stored cameras. Image-stored cameras (also in
         # ``camera_keys``) would KeyError, so restrict the list — and the
         # default — to video keys.
-        keys = list(self._meta.video_keys)
+        # Depth cameras are excluded from the annotation pipeline for now.
+        depth_keys = set(self._meta.depth_keys)
+        keys = [key for key in self._meta.video_keys if key not in depth_keys]
         # Last-resort fallback: if metadata didn't surface any video keys but
         # the caller explicitly named a camera (``--vlm.camera_key=...``),
         # trust them — the key is by definition known to exist on the dataset.
@@ -276,12 +278,12 @@ class VideoFrameProvider:
         from_timestamp = float(ep[f"videos/{self.camera_key}/from_timestamp"])
         to_timestamp = float(ep[f"videos/{self.camera_key}/to_timestamp"])
         src = self.root / self._meta.get_video_file_path(record.episode_index, self.camera_key)
-        encoder = VideoEncoderConfig(vcodec="h264", pix_fmt="yuv420p", g=None, crf=23, preset="ultrafast")
+        encoder = RGBEncoderConfig(vcodec="h264", pix_fmt="yuv420p", g=None, crf=23, preset="ultrafast")
         try:
             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/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
 
@@ -485,6 +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

@@ -128,6 +128,7 @@ class RealSenseCamera(Camera):
 
         self.fps = config.fps
         self.color_mode = config.color_mode
+        self.use_rgb = config.use_rgb
         self.use_depth = config.use_depth
         self.warmup_s = config.warmup_s
 
@@ -195,12 +196,15 @@ class RealSenseCamera(Camera):
         # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
         self.warmup_s = max(self.warmup_s, 1)
 
+        warmup_read = self.async_read if self.use_rgb else self.async_read_depth
         start_time = time.time()
         while time.time() - start_time < self.warmup_s:
-            self.async_read(timeout_ms=self.warmup_s * 1000)
+            warmup_read(timeout_ms=self.warmup_s * 1000)
             time.sleep(0.1)
         with self.frame_lock:
-            if self.latest_color_frame is None or self.use_depth and self.latest_depth_frame is None:
+            if (self.use_rgb and self.latest_color_frame is None) or (
+                self.use_depth and self.latest_depth_frame is None
+            ):
                 raise ConnectionError(f"{self} failed to capture frames during warmup.")
 
         logger.info(f"{self} connected.")
@@ -268,13 +272,13 @@ 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:
@@ -282,15 +286,17 @@ class RealSenseCamera(Camera):
         rs.config.enable_device(rs_config, self.serial_number)
 
         if self.width and self.height and self.fps:
-            rs_config.enable_stream(
+            if self.use_rgb:
-                rs.stream.color, self.capture_width, self.capture_height, rs.format.rgb8, self.fps
+                rs_config.enable_stream(
-            )
+                    rs.stream.color, self.capture_width, self.capture_height, rs.format.rgb8, self.fps
+                )
             if self.use_depth:
                 rs_config.enable_stream(
                     rs.stream.depth, self.capture_width, self.capture_height, rs.format.z16, self.fps
                 )
         else:
-            rs_config.enable_stream(rs.stream.color)
+            if self.use_rgb:
+                rs_config.enable_stream(rs.stream.color)
             if self.use_depth:
                 rs_config.enable_stream(rs.stream.depth)
 
@@ -298,8 +304,9 @@ class RealSenseCamera(Camera):
     def _configure_capture_settings(self) -> None:
         """Sets fps, width, and height from device stream if not already configured.
 
-        Uses the color stream profile to update unset attributes. Handles rotation by
+        Uses the color stream profile (or the depth stream profile when the color
-        swapping width/height when needed. Original capture dimensions are always stored.
+        stream is disabled) to update unset attributes. Handles rotation by swapping
+        width/height when needed. Original capture dimensions are always stored.
 
         Raises:
             DeviceNotConnectedError: If device is not connected.
@@ -308,7 +315,8 @@ class RealSenseCamera(Camera):
         if self.rs_profile is None:
             raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
 
-        stream = self.rs_profile.get_stream(rs.stream.color).as_video_stream_profile()
+        rs_stream = rs.stream.color if self.use_rgb else rs.stream.depth
+        stream = self.rs_profile.get_stream(rs_stream).as_video_stream_profile()
 
         if self.fps is None:
             self.fps = stream.fps()
@@ -323,6 +331,14 @@ class RealSenseCamera(Camera):
                 self.width, self.height = actual_width, actual_height
                 self.capture_width, self.capture_height = actual_width, actual_height
 
+    def _read(self, read_depth: bool = False) -> NDArray[Any]:
+        """Shared helper for :meth:`read`/:meth:`read_depth`: wait for a fresh color or depth frame."""
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
+
+        self.new_frame_event.clear()
+        return self._async_read(timeout_ms=10000, read_depth=read_depth)
+
     @check_if_not_connected
     def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
         """
@@ -332,8 +348,8 @@ 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.
@@ -349,20 +365,7 @@ class RealSenseCamera(Camera):
                 f"Failed to capture depth frame '.read_depth()'. Depth stream is not enabled for {self}."
             )
 
-        if self.thread is None or not self.thread.is_alive():
+        return self._read(read_depth=True)
-            raise RuntimeError(f"{self} read thread is not running.")
-
-        self.new_frame_event.clear()
-
-        _ = self.async_read(timeout_ms=10000)
-
-        with self.frame_lock:
-            depth_map = self.latest_depth_frame
-
-        if depth_map is None:
-            raise RuntimeError("No depth frame available. Ensure camera is streaming.")
-
-        return depth_map
 
     def _read_from_hardware(self):
         if self.rs_pipeline is None:
@@ -405,12 +408,10 @@ class RealSenseCamera(Camera):
                 f"{self} read() timeout_ms parameter is deprecated and will be removed in future versions."
             )
 
-        if self.thread is None or not self.thread.is_alive():
+        if not self.use_rgb:
-            raise RuntimeError(f"{self} read thread is not running.")
+            raise RuntimeError(f"{self}: cannot read color — camera was configured with use_rgb=False.")
 
-        self.new_frame_event.clear()
+        frame = self._read()
-
-        frame = self.async_read(timeout_ms=10000)
 
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
         logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
@@ -465,8 +466,8 @@ 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.
@@ -479,19 +480,24 @@ class RealSenseCamera(Camera):
         while not stop_event.is_set():
             try:
                 frame = self._read_from_hardware()
-                color_frame_raw = frame.get_color_frame()
+
-                color_frame = np.asanyarray(color_frame_raw.get_data())
+                if self.use_rgb:
-                processed_color_frame = self._postprocess_image(color_frame)
+                    color_frame_raw = frame.get_color_frame()
+                    color_frame = np.asanyarray(color_frame_raw.get_data())
+                    processed_color_frame = self._postprocess_image(color_frame)
 
                 if self.use_depth:
                     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()
 
                 with self.frame_lock:
-                    self.latest_color_frame = processed_color_frame
+                    if self.use_rgb:
+                        self.latest_color_frame = processed_color_frame
                     if self.use_depth:
                         self.latest_depth_frame = processed_depth_frame
                     self.latest_timestamp = capture_time
@@ -523,6 +529,8 @@ 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
@@ -533,7 +541,26 @@ class RealSenseCamera(Camera):
             self.latest_timestamp = None
             self.new_frame_event.clear()
 
-    # NOTE(Steven): Missing implementation for depth for now
+    def _async_read(self, timeout_ms: float, read_depth: bool = False) -> NDArray[Any]:
+        """Shared helper for :meth:`async_read`/:meth:`async_read_depth`: return the latest buffered frame."""
+        if self.thread is None or not self.thread.is_alive():
+            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 frame from camera {self} after {timeout_ms} ms. "
+                f"Read thread alive: {self.thread.is_alive()}."
+            )
+
+        with self.frame_lock:
+            frame = self.latest_depth_frame if read_depth else self.latest_color_frame
+            self.new_frame_event.clear()
+
+        if frame is None:
+            raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
+
+        return frame
+
     @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
@@ -558,25 +585,31 @@ class RealSenseCamera(Camera):
             RuntimeError: If the background thread died unexpectedly or another error occurs.
         """
 
+        if not self.use_rgb:
+            raise RuntimeError(f"{self}: cannot read color — camera was configured with use_rgb=False.")
+
+        return self._async_read(timeout_ms=timeout_ms)
+
+    def _read_latest(self, max_age_ms: int, read_depth: bool = False) -> NDArray[Any]:
+        """Shared helper for :meth:`read_latest`/:meth:`read_latest_depth`: peek the latest buffered frame."""
         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 frame from camera {self} after {timeout_ms} ms. "
-                f"Read thread alive: {self.thread.is_alive()}."
-            )
-
         with self.frame_lock:
-            frame = self.latest_color_frame
+            frame = self.latest_depth_frame if read_depth else self.latest_color_frame
-            self.new_frame_event.clear()
+            timestamp = self.latest_timestamp
 
-        if frame is None:
+        if frame is None or timestamp is None:
-            raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
+            raise RuntimeError(f"{self} has not captured any frames yet.")
+
+        age_ms = (time.perf_counter() - timestamp) * 1e3
+        if age_ms > max_age_ms:
+            raise TimeoutError(
+                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
+            )
 
         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).
@@ -593,24 +626,48 @@ class RealSenseCamera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If the camera is connected but has not captured any frames yet.
         """
+        if not self.use_rgb:
+            raise RuntimeError(f"{self}: cannot read color — camera was configured with use_rgb=False.")
 
-        if self.thread is None or not self.thread.is_alive():
+        return self._read_latest(max_age_ms=max_age_ms)
-            raise RuntimeError(f"{self} read thread is not running.")
 
-        with self.frame_lock:
+    @check_if_not_connected
-            frame = self.latest_color_frame
+    def async_read_depth(self, timeout_ms: float = 200) -> NDArray[np.uint16]:
-            timestamp = self.latest_timestamp
+        """Read the latest depth frame asynchronously, in millimeters.
 
-        if frame is None or timestamp is None:
+        Mirrors :meth:`async_read` but returns the depth stream rather than the
-            raise RuntimeError(f"{self} has not captured any frames yet.")
+        color stream. Output is ``np.uint16`` of shape ``(H, W, 1)``, where each
+        pixel is the distance from the sensor in millimeters.
 
-        age_ms = (time.perf_counter() - timestamp) * 1e3
+        Raises:
-        if age_ms > max_age_ms:
+            DeviceNotConnectedError: If the camera is not connected.
-            raise TimeoutError(
+            RuntimeError: If ``use_depth`` is ``False`` for this camera, or if
-                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
+                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.")
 
-        return frame
+        return self._async_read(timeout_ms=timeout_ms, read_depth=True)
+
+    @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.")
+
+        return self._read_latest(max_age_ms=max_age_ms, read_depth=True)
 
     def disconnect(self) -> None:
         """

src/lerobot/cameras/realsense/configuration_realsense.py

@@ -42,12 +42,14 @@ class RealSenseCameraConfig(CameraConfig):
         height: Requested frame height in pixels for the color stream.
         serial_number_or_name: Unique serial number or human-readable name to identify the camera.
         color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
+        use_rgb: Whether to enable the color stream. Defaults to True.
         use_depth: Whether to enable depth stream. Defaults to False.
         rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
         warmup_s: Time reading frames before returning from connect (in seconds)
 
     Note:
         - Either name or serial_number must be specified.
+        - At least one of `use_rgb` or `use_depth` must be enabled.
         - Depth stream configuration (if enabled) will use the same FPS as the color stream.
         - The actual resolution and FPS may be adjusted by the camera to the nearest supported mode.
         - For `fps`, `width` and `height`, either all of them need to be set, or none of them.
@@ -55,6 +57,7 @@ class RealSenseCameraConfig(CameraConfig):
 
     serial_number_or_name: str
     color_mode: ColorMode = ColorMode.RGB
+    use_rgb: bool = True
     use_depth: bool = False
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
     warmup_s: int = 1
@@ -63,6 +66,9 @@ class RealSenseCameraConfig(CameraConfig):
         self.color_mode = ColorMode(self.color_mode)
         self.rotation = Cv2Rotation(self.rotation)
 
+        if not self.use_rgb and not self.use_depth:
+            raise ValueError("At least one of `use_rgb` or `use_depth` must be enabled.")
+
         values = (self.fps, self.width, self.height)
         if any(v is not None for v in values) and any(v is None for v in values):
             raise ValueError(

src/lerobot/cameras/zmq/camera_zmq.py

@@ -293,6 +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/configs/__init__.py

@@ -33,10 +33,15 @@ from .types import (
     RTCAttentionSchedule,
 )
 from .video import (
+    DEFAULT_DEPTH_UNIT,
     VALID_VIDEO_CODECS,
     VIDEO_ENCODER_INFO_KEYS,
+    DepthEncoderConfig,
+    RGBEncoderConfig,
     VideoEncoderConfig,
-    camera_encoder_defaults,
+    depth_encoder_defaults,
+    encoder_config_from_video_info,
+    rgb_encoder_defaults,
 )
 
 __all__ = [
@@ -57,9 +62,15 @@ __all__ = [
     "WandBConfig",
     "load_recipe",
     "VideoEncoderConfig",
+    "RGBEncoderConfig",
+    "DepthEncoderConfig",
     # Defaults
-    "camera_encoder_defaults",
+    "rgb_encoder_defaults",
+    "depth_encoder_defaults",
+    # Factories
+    "encoder_config_from_video_info",
     # Constants
+    "DEFAULT_DEPTH_UNIT",
     "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, RGBEncoderConfig, depth_encoder_defaults, rgb_encoder_defaults
 
 
 @dataclass
@@ -58,8 +58,10 @@ class DatasetRecordConfig:
     # Set to 1 for immediate encoding (default behavior), or higher for batched encoding
     video_encoding_batch_size: int = 1
     # Video encoder settings for camera MP4s (codec, quality, GOP, etc.). Tuned via CLI nested keys,
-    # e.g. ``--dataset.camera_encoder.vcodec=h264`` (see ``VideoEncoderConfig``).
+    # e.g. ``--dataset.rgb_encoder.vcodec=h264`` (see ``RGBEncoderConfig``).
-    camera_encoder: VideoEncoderConfig = field(default_factory=camera_encoder_defaults)
+    rgb_encoder: RGBEncoderConfig = field(default_factory=rgb_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

@@ -19,6 +19,8 @@ from dataclasses import dataclass, field
 from lerobot.transforms import ImageTransformsConfig
 from lerobot.utils.import_utils import get_safe_default_video_backend
 
+from .video import DEFAULT_DEPTH_UNIT, DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT
+
 
 @dataclass
 class DatasetConfig:
@@ -35,14 +37,21 @@ 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" (millimeters) or "m" (metres).
+    # Has no effect on datasets without depth cameras.
+    depth_output_unit: str = DEFAULT_DEPTH_UNIT
     streaming: bool = False
     # Fraction of episodes held out per task for offline evaluation (0.0 = disabled).
     eval_split: float = 0.0
 
     def __post_init__(self) -> None:
+        if self.depth_output_unit not in (DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT):
+            raise ValueError(
+                f"depth_output_unit must be '{DEPTH_METER_UNIT}' or '{DEPTH_MILLIMETER_UNIT}', got {self.depth_output_unit!r}"
+            )
         if not (0.0 <= self.eval_split < 1.0):
             raise ValueError(f"eval_split must be in [0.0, 1.0), got {self.eval_split}")
         if self.episodes is not None:

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
 
@@ -40,7 +40,6 @@ VALID_VIDEO_CODECS: frozenset[str] = frozenset({"h264", "hevc", "libsvtav1", "au
 # 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`).
@@ -52,40 +51,45 @@ 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_METER_UNIT: str = "m"
+DEPTH_MILLIMETER_UNIT: str = "mm"
+DEFAULT_DEPTH_UNIT: str = DEPTH_MILLIMETER_UNIT
+
+# 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:
-    """Video encoder configuration.
+    """Video encoder configuration."""
 
-    Attributes:
+    vcodec: str = "libsvtav1"  # Video codec name. "auto" picks a hardware codec if available, else libsvtav1.
-        vcodec: Video encoder name. ``"auto"`` is resolved during
+    pix_fmt: str = "yuv420p"  # Pixel format (e.g. yuv420p).
-            construction (HW encoder if available, else ``libsvtav1``).
+    g: int | None = 2  # GOP size (keyframe interval).
-        pix_fmt: Pixel format (e.g. ``"yuv420p"``).
+    crf: int | float | None = 30  # Quality level. Lower means better quality and larger files.
-        g: GOP size (keyframe interval).
+    preset: int | str | None = None  # Speed/quality preset. Accepted values are codec-specific.
-        crf: Quality level — mapped to the native quality parameter of the
+    fast_decode: int = 0  # Fast-decode tuning. Accepted values are codec-specific, 0 disables it.
-            codec (``crf`` for software, ``qp`` for NVENC/VAAPI,
-            ``q:v`` for VideoToolbox, ``global_quality`` for QSV).
-        preset: Speed/quality preset. Accepted type is per-codec.
-        fast_decode: Fast-decode tuning. For ``libsvtav1`` this is a level (0-2)
-            embedded in ``svtav1-params``. For ``h264`` and ``hevc`` non-zero values
-            set ``tune=fastdecode``. Ignored for other codecs.
-        video_backend: Python to be used for encoding. Only ``"pyav"``
-            is currently supported.
-        extra_options: Free-form dictionary of additional video encoder options
-            (e.g. ``{"tune": "film", "profile:v": "high", "bf": 2}``).
-    """
-
-    vcodec: str = "libsvtav1"  # TODO(CarolinePascal): rename to codec ?
-    pix_fmt: str = "yuv420p"
-    g: int | None = 2
-    crf: int | float | None = 30
-    preset: int | str | None = None
-    fast_decode: int = 0
     # TODO(CarolinePascal): add torchcodec support + find a way to unify the
     # two backends (encoding and decoding).
-    video_backend: str = "pyav"
+    video_backend: str = "pyav"  # Encoding backend. Only "pyav" is currently supported.
+    # Extra codec options merged last, e.g. {"tune": "film"}.
     extra_options: dict[str, Any] = field(default_factory=dict)
 
+    # Source-data channel count this encoder is expected to handle. ``None``
+    # disables the pix_fmt channel-count check; concrete subclasses set it
+    # (3 for RGB, 1 for depth, etc.).
+    _DEFAULT_CHANNELS: ClassVar[int | None] = None
+
     def __post_init__(self) -> None:
         self.resolve_vcodec()
         # Empty-constructor ergonomics: ``VideoEncoderConfig()`` must "just work".
@@ -94,9 +98,9 @@ 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 +119,15 @@ 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 +150,9 @@ 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.
@@ -230,6 +244,79 @@ class VideoEncoderConfig:
         return opts
 
 
-def camera_encoder_defaults() -> VideoEncoderConfig:
+@dataclass
-    """Return a :class:`VideoEncoderConfig` with RGB-camera defaults."""
+class RGBEncoderConfig(VideoEncoderConfig):
-    return VideoEncoderConfig()
+    """Encoder configuration for RGB camera streams.
+
+    Identical to :class:`VideoEncoderConfig` but declares the 3-channel
+    source-data layout so ``pix_fmt`` is validated against RGB inputs.
+    """
+
+    _DEFAULT_CHANNELS: ClassVar[int] = 3
+
+
+def rgb_encoder_defaults() -> RGBEncoderConfig:
+    """Return a :class:`RGBEncoderConfig` with RGB-camera defaults."""
+    return RGBEncoderConfig()
+
+
+@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 parameters of the depth quantizer.
+    Defaults flip ``vcodec`` to ``"hevc"`` (Main 12 profile) and ``pix_fmt`` to
+    ``"gray12le"``.
+    """
+
+    vcodec: str = "hevc"  # Video codec name. Defaults to HEVC Main 12 (a 12-bit-capable codec).
+    pix_fmt: str = "gray12le"  # Pixel format. Defaults to 12-bit grayscale.
+    extra_options: dict[str, Any] = field(default_factory=lambda: {"x265-params": "lossless=1"})
+
+    depth_min: float = DEFAULT_DEPTH_MIN  # Minimum depth in meters, mapped to the lowest quantum.
+    depth_max: float = DEFAULT_DEPTH_MAX  # Maximum depth in meters, mapped to the highest quantum.
+    shift: float = DEFAULT_DEPTH_SHIFT  # Pre-log offset in meters for numerical stability near zero.
+    use_log: bool = DEFAULT_DEPTH_USE_LOG  # Use logarithmic quantization (True) or linear (False).
+
+    _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:`RGBEncoderConfig`
+    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:`RGBEncoderConfig`.
+    """
+    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 RGBEncoderConfig
+    return cls.from_video_info(video_info)

src/lerobot/datasets/compute_stats.py

@@ -242,12 +242,12 @@ def sample_images(image_paths: list[str]) -> np.ndarray:
     images = None
     for i, idx in enumerate(sampled_indices):
         path = image_paths[idx]
-        # we load as uint8 to reduce memory usage
+        # we load RGB images as uint8 to reduce memory usage; depth keeps its native dtype
         img = load_image_as_numpy(path, dtype=np.uint8, channel_first=True)
         img = auto_downsample_height_width(img)
 
         if images is None:
-            images = np.empty((len(sampled_indices), *img.shape), dtype=np.uint8)
+            images = np.empty((len(sampled_indices), *img.shape), dtype=img.dtype)
 
         images[i] = img
 
@@ -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
+import logging
+from collections.abc import Callable, Iterable
 from copy import deepcopy
 from pathlib import Path
 
@@ -338,6 +339,25 @@ 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)."""
@@ -581,29 +601,48 @@ 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}
+            merged = {**existing, **new_info}
+            # Migrate the legacy depth marker to the canonical key.
+            if "video.is_depth_map" in merged:
+                logging.warning(
+                    f"Migrating legacy 'video.is_depth_map' to 'is_depth_map' for feature {key!r}."
+                )
+                merged.setdefault("is_depth_map", merged.pop("video.is_depth_map"))
+            self.info.features[key]["info"] = merged
 
     def update_chunk_settings(
         self,

src/lerobot/datasets/dataset_reader.py

@@ -22,7 +22,10 @@ from pathlib import Path
 import datasets
 import torch
 
+from lerobot.configs import DEFAULT_DEPTH_UNIT, 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 = DEFAULT_DEPTH_UNIT,
     ):
         """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
@@ -78,6 +86,7 @@ class DatasetReader:
             raise TypeError("image_transforms must be callable or None.")
         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
@@ -88,6 +97,11 @@ class DatasetReader:
             check_delta_timestamps(delta_timestamps, meta.fps, tolerance_s)
             self.delta_indices = get_delta_indices(delta_timestamps, meta.fps)
 
+        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 set_image_transforms(self, image_transforms: Callable | None) -> None:
         """Replace the transform applied to visual observations."""
         if image_transforms is not None and not callable(image_transforms):
@@ -259,7 +273,18 @@ 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())
@@ -299,8 +324,9 @@ class DatasetReader:
             item = {**video_frames, **item}
 
         if self._image_transforms is not None:
-            image_keys = self._meta.camera_keys
+            for cam in self._meta.camera_keys:
-            for cam in image_keys:
+                if cam in self._meta.depth_keys:
+                    continue
                 item[cam] = self._image_transforms(item[cam])
 
         # Add task as a string

src/lerobot/datasets/dataset_tools.py

@@ -37,7 +37,15 @@ import pyarrow.parquet as pq
 import torch
 from tqdm import tqdm
 
-from lerobot.configs import VideoEncoderConfig, camera_encoder_defaults
+from lerobot.configs import (
+    DepthEncoderConfig,
+    RGBEncoderConfig,
+    VideoEncoderConfig,
+    depth_encoder_defaults,
+    encoder_config_from_video_info,
+    rgb_encoder_defaults,
+)
+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
 
@@ -48,6 +56,7 @@ 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,
@@ -62,12 +71,13 @@ 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,
 )
 
@@ -601,7 +611,7 @@ 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.
 
@@ -615,7 +625,7 @@ 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
 
@@ -640,13 +650,13 @@ 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))
@@ -733,7 +743,7 @@ 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")
         )
 
@@ -817,7 +827,7 @@ def _copy_and_reindex_videos(
                     dst_video_path,
                     episodes_to_keep_ranges,
                     src_dataset.meta.fps,
-                    camera_encoder,
+                    video_encoder,
                 )
 
                 cumulative_ts = 0.0
@@ -874,11 +884,11 @@ def _copy_and_reindex_episodes_metadata(
             episode_meta.update(video_metadata[new_idx])
 
         # Extract episode statistics from parquet metadata.
-        # Note (maractingi): When pandas/pyarrow serializes numpy arrays with shape (3, 1, 1) to parquet,
+        # When pandas/pyarrow serializes numpy arrays with shape (C, 1, 1) to parquet,
         # they are being deserialized as nested object arrays like:
         #   array([array([array([0.])]), array([array([0.])]), array([array([0.])])])
         # This happens particularly with image/video statistics. We need to detect and flatten
-        # these nested structures back to proper (3, 1, 1) arrays so aggregate_stats can process them.
+        # these nested structures back to proper (C, 1, 1) arrays so aggregate_stats can process them.
         episode_stats = {}
         for key in src_episode_full:
             if key.startswith("stats/"):
@@ -894,15 +904,16 @@ def _copy_and_reindex_episodes_metadata(
                     if feature_name in src_dataset.meta.features:
                         feature_dtype = src_dataset.meta.features[feature_name]["dtype"]
                         if feature_dtype in ["image", "video"] and stat_name != "count":
+                            # Stats are channel-first (C, 1, 1)
                             if isinstance(value, np.ndarray) and value.dtype == object:
                                 flat_values = []
                                 for item in value:
                                     while isinstance(item, np.ndarray):
                                         item = item.flatten()[0]
                                     flat_values.append(item)
-                                value = np.array(flat_values, dtype=np.float64).reshape(3, 1, 1)
+                                value = np.array(flat_values, dtype=np.float64).reshape(-1, 1, 1)
-                            elif isinstance(value, np.ndarray) and value.shape == (3,):
+                            elif isinstance(value, np.ndarray) and value.ndim == 1:
-                                value = value.reshape(3, 1, 1)
+                                value = value.reshape(-1, 1, 1)
 
                     episode_stats[feature_name][stat_name] = value
 
@@ -1153,15 +1164,15 @@ 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:
@@ -1193,13 +1204,14 @@ 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 = []
@@ -1290,7 +1302,7 @@ 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.
@@ -1304,7 +1316,7 @@ 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:
@@ -1329,10 +1341,11 @@ 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"
@@ -1340,7 +1353,7 @@ 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,
         )
 
@@ -1613,6 +1626,7 @@ 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"):
@@ -1658,7 +1672,8 @@ def convert_image_to_video_dataset(
     dataset: LeRobotDataset,
     output_dir: Path | None = None,
     repo_id: str | None = None,
-    camera_encoder: VideoEncoderConfig | None = None,
+    rgb_encoder: RGBEncoderConfig | None = None,
+    depth_encoder: DepthEncoderConfig | None = None,
     episode_indices: list[int] | None = None,
     num_workers: int = 4,
     max_episodes_per_batch: int | None = None,
@@ -1670,21 +1685,32 @@ 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)
+        rgb_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.rgb_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:
+    if rgb_encoder is None:
-        camera_encoder = camera_encoder_defaults()
+        rgb_encoder = rgb_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:
@@ -1709,10 +1735,7 @@ 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: {rgb_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 = {}
@@ -1774,6 +1797,8 @@ 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 rgb_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(
@@ -1782,7 +1807,7 @@ 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}")
@@ -1824,7 +1849,7 @@ 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,
                 )
 
@@ -1863,16 +1888,11 @@ 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 rgb_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)
 
@@ -1899,11 +1919,11 @@ 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,
     )
@@ -1912,7 +1932,8 @@ def _reencode_video_worker(args: tuple) -> Path:
 
 def reencode_dataset(
     dataset: LeRobotDataset,
-    camera_encoder: VideoEncoderConfig,
+    rgb_encoder: RGBEncoderConfig | None = None,
+    depth_encoder: DepthEncoderConfig | None = None,
     encoder_threads: int | None = None,
     num_workers: int | None = None,
 ) -> LeRobotDataset:
@@ -1923,8 +1944,11 @@ def reencode_dataset(
     Args:
         dataset: An existing :class:`LeRobotDataset` whose videos will be
             re-encoded.
-        camera_encoder: Target encoder configuration applied to every video
+        rgb_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
@@ -1936,23 +1960,35 @@ def reencode_dataset(
         on disk.
     """
     meta = dataset.meta
-    video_paths_list = []
+    video_keys_encoders_dict = {}
+    video_keys_paths_dict = {}
+
+    if rgb_encoder is None and depth_encoder is None:
+        raise ValueError("Either rgb_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 rgb_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]
@@ -1966,10 +2002,15 @@ 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 +31,13 @@ import PIL.Image
 import pyarrow.parquet as pq
 import torch
 
-from lerobot.configs import VideoEncoderConfig, camera_encoder_defaults
+from lerobot.configs import (
+    DepthEncoderConfig,
+    RGBEncoderConfig,
+    VideoEncoderConfig,
+    depth_encoder_defaults,
+    rgb_encoder_defaults,
+)
 
 from .compute_stats import compute_episode_stats
 from .dataset_metadata import LeRobotDatasetMetadata
@@ -48,6 +54,7 @@ from .io_utils import (
     write_info,
 )
 from .utils import (
+    DEFAULT_DEPTH_PATH,
     DEFAULT_EPISODES_PATH,
     DEFAULT_IMAGE_PATH,
     update_chunk_file_indices,
@@ -67,17 +74,22 @@ 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,
     )
@@ -96,7 +108,8 @@ class DatasetWriter:
         self,
         meta: LeRobotDatasetMetadata,
         root: Path,
-        camera_encoder: VideoEncoderConfig | None,
+        rgb_encoder: RGBEncoderConfig | None,
+        depth_encoder: DepthEncoderConfig | None,
         encoder_threads: int | None,
         batch_encoding_size: int,
         streaming_encoder: StreamingVideoEncoder | None = None,
@@ -108,8 +121,11 @@ 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.
+            rgb_encoder: Video encoder settings applied to RGB cameras. When
-                ``None`` uses :func:`~lerobot.configs.camera_encoder_defaults`.
+                ``None``, :func:`~lerobot.configs.video.rgb_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
@@ -120,7 +136,8 @@ class DatasetWriter:
         """
         self._meta = meta
         self._root = root
-        self._camera_encoder = camera_encoder or camera_encoder_defaults()
+        self._rgb_encoder = rgb_encoder or rgb_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 +162,8 @@ 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 +213,7 @@ 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 +301,13 @@ 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 +322,7 @@ 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._rgb_encoder,
                             self._encoder_threads,
                         ): video_key
                         for video_key in self._meta.video_keys
@@ -511,7 +533,12 @@ 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._rgb_encoder,
+            )
             write_info(self._meta.info, self._meta.root)
 
         metadata = {
@@ -578,13 +605,14 @@ 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._rgb_encoder,
             self._encoder_threads,
         )
 

src/lerobot/datasets/depth_utils.py

@@ -0,0 +1,268 @@
+#!/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:`DepthEncoderConfig`.
+"""
+
+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_METER_UNIT,
+    DEPTH_MILLIMETER_UNIT,
+    DEPTH_QMAX,
+)
+
+from .image_writer import squeeze_single_channel
+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", DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT],
+) -> tuple[NDArray[np.float32], Literal[DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT]]:
+    """Convert depth to float32 in the chosen unit, and return the resolved unit."""
+    resolved_unit = (
+        (DEPTH_METER_UNIT if np.issubdtype(depth.dtype, np.floating) else DEPTH_MILLIMETER_UNIT)
+        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", DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT] = "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 (e.g. HEVC Main 12).
+    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", DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT):
+        raise ValueError(
+            f"input_unit must be 'auto', '{DEPTH_METER_UNIT}', or '{DEPTH_MILLIMETER_UNIT}', 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)
+    depth = squeeze_single_channel(depth)
+
+    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 == DEPTH_METER_UNIT else np.float32(depth_min * _MM_PER_METRE)
+    )
+    depth_max_u = (
+        np.float32(depth_max) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_max * _MM_PER_METRE)
+    )
+    shift_u = np.float32(shift) if resolved_unit == DEPTH_METER_UNIT 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[DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT] = DEPTH_MILLIMETER_UNIT,
+    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 (DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT):
+        raise ValueError(
+            f"output_unit must be '{DEPTH_METER_UNIT}' or '{DEPTH_MILLIMETER_UNIT}', 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 == DEPTH_METER_UNIT:
+            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 == DEPTH_METER_UNIT:
+        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

@@ -97,6 +97,7 @@ 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:
@@ -127,6 +128,8 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
 
     if cfg.dataset.use_imagenet_stats:
         for key in dataset.meta.camera_keys:
+            if key in dataset.meta.depth_keys:
+                continue  # Exclude depth keys from ImageNet stats
             for stats_type, stats in IMAGENET_STATS.items():
                 dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
 

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

@@ -41,11 +41,51 @@ def safe_stop_image_writer(func):
     return wrapper
 
 
-def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True) -> PIL.Image.Image:
+def squeeze_single_channel(array: np.ndarray) -> np.ndarray:
-    # TODO(aliberts): handle 1 channel and 4 for depth images
+    """Drop a leading or trailing singleton channel dim: ``(1, H, W)`` / ``(H, W, 1)`` -> ``(H, W)``.
-    if image_array.ndim != 3:
-        raise ValueError(f"The array has {image_array.ndim} dimensions, but 3 is expected for an image.")
 
+    Unlike ``array.squeeze()``, this only removes the channel axis, never an ``H`` or ``W`` of size 1.
+    """
+    if array.ndim == 3:
+        if array.shape[0] == 1:
+            return array[0]
+        if array.shape[-1] == 1:
+            return array[..., 0]
+    return array
+
+
+def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True) -> PIL.Image.Image:
+    """Convert a NumPy array to a PIL Image, preserving precision for grayscale.
+
+    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).
+    image_array = squeeze_single_channel(image_array)
+
+    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 +111,29 @@ 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"}
+    else:
+        raise ValueError(f"Unsupported image file extension: {suffix}")
+
+
 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 +157,7 @@ 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

@@ -226,28 +226,50 @@ def load_image_as_numpy(
     Args:
         fpath (str | Path): Path to the image file.
         dtype (np.dtype): The desired data type of the output array. If floating,
-            pixels are scaled to [0, 1].
+            pixels are scaled to [0, 1]. Only used for RGB images.
         channel_first (bool): If True, converts the image to (C, H, W) format.
             Otherwise, it remains in (H, W, C) format.
 
     Returns:
         np.ndarray: The image as a numpy array.
     """
-    img = PILImage.open(fpath).convert("RGB")
+    is_depth = fpath.endswith(".tiff") or fpath.endswith(".tif")
-    img_array = np.array(img, dtype=dtype)
+    if is_depth:
+        # Preserve the native depth dtype (uint16 -> "I;16", float32 -> "F").
+        img = PILImage.open(fpath)
+        img_array = np.array(img)
+    else:
+        img = PILImage.open(fpath).convert("RGB")
+        img_array = np.array(img, dtype=dtype)
+        if np.issubdtype(dtype, np.floating):
+            img_array /= 255.0
     if channel_first:  # (H, W, C) -> (C, H, W)
-        img_array = np.transpose(img_array, (2, 0, 1))
+        img_array = img_array[np.newaxis, ...] if img_array.ndim == 2 else np.transpose(img_array, (2, 0, 1))
-    if np.issubdtype(dtype, np.floating):
-        img_array /= 255.0
     return img_array
 
 
+# PIL modes for 16-bit unsigned depth maps.
+UINT16_PIL_MODES = {"I;16", "I;16B", "I;16L"}
+
+
+def pil_to_chw_tensor(img: PILImage.Image) -> torch.Tensor:
+    """Convert a PIL image to a channel-first tensor.
+
+    ``uint16`` depth maps become ``float32 (1, H, W)`` in native units (``ToTensor``
+    would overflow them to ``int16``); all other modes use the standard ``ToTensor`` path.
+    """
+    if img.mode in UINT16_PIL_MODES:
+        return torch.from_numpy(np.array(img, dtype=np.float32))[None, ...]
+    return transforms.ToTensor()(img)
+
+
 def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[torch.Tensor | str]]:
     """Convert a batch from a Hugging Face dataset to torch tensors.
 
     This transform function converts items from Hugging Face dataset format (pyarrow)
-    to torch tensors. Importantly, images are converted from PIL objects (H, W, C, uint8)
+    to torch tensors. RGB images are converted from PIL objects (H, W, C, uint8)
-    to a torch image representation (C, H, W, float32) in the range [0, 1]. Other
+    to a torch image representation (C, H, W, float32) in the range [0, 1]. Depth
+    maps are returned as float32 (1, H, W) in their native units. Other
     types are converted to torch.tensor.
 
     Args:
@@ -262,8 +284,7 @@ def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[to
             continue
         first_item = items_dict[key][0]
         if isinstance(first_item, PILImage.Image):
-            to_tensor = transforms.ToTensor()
+            items_dict[key] = [pil_to_chw_tensor(img) for img in items_dict[key]]
-            items_dict[key] = [to_tensor(img) for img in items_dict[key]]
         elif first_item is None or isinstance(first_item, dict):
             pass
         else:
@@ -329,7 +350,11 @@ def item_to_torch(item: dict) -> dict:
     """
     skip_keys = {"task", *LANGUAGE_COLUMNS}
     for key, val in item.items():
-        if isinstance(val, (np.ndarray | list)) and key not in skip_keys:
+        if key in skip_keys:
+            continue
+        if isinstance(val, PILImage.Image):
+            item[key] = pil_to_chw_tensor(val)
+        elif isinstance(val, (np.ndarray | list)):
             # Convert numpy arrays and lists to torch tensors
             item[key] = torch.tensor(val)
     return item

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 DEFAULT_DEPTH_UNIT, DepthEncoderConfig, RGBEncoderConfig
 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 = DEFAULT_DEPTH_UNIT,
         batch_encoding_size: int = 1,
-        camera_encoder: VideoEncoderConfig | None = None,
+        rgb_encoder: RGBEncoderConfig | None = None,
+        depth_encoder: DepthEncoderConfig | None = None,
         encoder_threads: int | None = None,
         streaming_encoding: bool = False,
         encoder_queue_maxsize: int = 30,
@@ -183,8 +185,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 You can also use the 'pyav' decoder used by Torchvision, which used to be the default option, or 'video_reader' which is another decoder of Torchvision.
             batch_encoding_size (int, optional): Number of episodes to accumulate before batch encoding videos.
                 Set to 1 for immediate encoding (default), or higher for batched encoding. Defaults to 1.
-            camera_encoder (VideoEncoderConfig | None, optional): Video encoder settings for cameras
+            rgb_encoder (RGBEncoderConfig | None, optional): Video encoder settings for cameras
-                (codec, quality, etc.). When ``None``, :func:`~lerobot.configs.video.camera_encoder_defaults`
+                (codec, quality, etc.). When ``None``, :func:`~lerobot.configs.video.rgb_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.
@@ -206,6 +211,7 @@ 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
 
@@ -246,6 +252,7 @@ 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,
         )
         self.image_transforms = image_transforms
 
@@ -271,14 +278,16 @@ class LeRobotDataset(torch.utils.data.Dataset):
             if streaming_encoding and len(self.meta.video_keys) > 0:
                 streaming_enc = self._build_streaming_encoder(
                     self.meta.fps,
-                    camera_encoder,
+                    rgb_encoder,
+                    depth_encoder,
                     encoder_queue_maxsize,
                     encoder_threads,
                 )
             self.writer = DatasetWriter(
                 meta=self.meta,
                 root=self.root,
-                camera_encoder=camera_encoder,
+                rgb_encoder=rgb_encoder,
+                depth_encoder=depth_encoder,
                 encoder_threads=encoder_threads,
                 batch_encoding_size=batch_encoding_size,
                 streaming_encoder=streaming_enc,
@@ -314,19 +323,22 @@ 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
 
     @staticmethod
     def _build_streaming_encoder(
         fps: int,
-        camera_encoder: VideoEncoderConfig | None,
+        rgb_encoder: RGBEncoderConfig | None,
+        depth_encoder: DepthEncoderConfig | None,
         encoder_queue_maxsize: int,
         encoder_threads: int | None,
     ) -> StreamingVideoEncoder:
         return StreamingVideoEncoder(
             fps=fps,
-            camera_encoder=camera_encoder,
+            rgb_encoder=rgb_encoder,
+            depth_encoder=depth_encoder,
             queue_maxsize=encoder_queue_maxsize,
             encoder_threads=encoder_threads,
         )
@@ -655,7 +667,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         image_writer_threads: int = 0,
         video_backend: str | None = None,
         batch_encoding_size: int = 1,
-        camera_encoder: VideoEncoderConfig | None = None,
+        rgb_encoder: RGBEncoderConfig | None = None,
+        depth_encoder: DepthEncoderConfig | None = None,
         metadata_buffer_size: int = 10,
         streaming_encoding: bool = False,
         encoder_queue_maxsize: int = 30,
@@ -686,8 +699,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
             video_backend: Video decoding backend (used when reading back).
             batch_encoding_size: Number of episodes to accumulate before
                 batch-encoding videos. ``1`` means encode immediately.
-            camera_encoder: Video encoder settings for cameras (codec, quality, etc.).
+            rgb_encoder: Video encoder settings for cameras (codec, quality, etc.).
-                When ``None``, :func:`~lerobot.configs.video.camera_encoder_defaults` is used.
+                When ``None``, :func:`~lerobot.configs.video.rgb_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
@@ -722,6 +737,7 @@ 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 = DEFAULT_DEPTH_UNIT
         obj._batch_encoding_size = batch_encoding_size
         obj._encoder_threads = encoder_threads
 
@@ -731,12 +747,13 @@ 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, rgb_encoder, depth_encoder, encoder_queue_maxsize, encoder_threads
             )
         obj.writer = DatasetWriter(
             meta=obj.meta,
             root=obj.root,
-            camera_encoder=camera_encoder,
+            rgb_encoder=rgb_encoder,
+            depth_encoder=depth_encoder,
             encoder_threads=encoder_threads,
             batch_encoding_size=batch_encoding_size,
             streaming_encoder=streaming_enc,
@@ -759,7 +776,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         force_cache_sync: bool = False,
         video_backend: str | None = None,
         batch_encoding_size: int = 1,
-        camera_encoder: VideoEncoderConfig | None = None,
+        rgb_encoder: RGBEncoderConfig | None = None,
+        depth_encoder: DepthEncoderConfig | None = None,
         encoder_threads: int | None = None,
         image_writer_processes: int = 0,
         image_writer_threads: int = 0,
@@ -787,8 +805,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
             video_backend: Video decoding backend for reading back data.
             batch_encoding_size: Number of episodes to accumulate before
                 batch-encoding videos.
-            camera_encoder: Video encoder settings for cameras (codec, quality, etc.).
+            rgb_encoder: Video encoder settings for cameras (codec, quality, etc.).
-                When ``None``, :func:`~lerobot.configs.video.camera_encoder_defaults` is used.
+                When ``None``, :func:`~lerobot.configs.video.rgb_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.
@@ -816,6 +836,7 @@ 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 = DEFAULT_DEPTH_UNIT
         obj._batch_encoding_size = batch_encoding_size
 
         if obj._requested_root is not None:
@@ -835,12 +856,13 @@ 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, rgb_encoder, depth_encoder, encoder_queue_maxsize, encoder_threads
             )
         obj.writer = DatasetWriter(
             meta=obj.meta,
             root=obj.root,
-            camera_encoder=camera_encoder,
+            rgb_encoder=rgb_encoder,
+            depth_encoder=depth_encoder,
             encoder_threads=encoder_threads,
             batch_encoding_size=batch_encoding_size,
             streaming_encoder=streaming_enc,

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 +32,34 @@ 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 a 2D ``uint16`` image into the plane's memory buffer, row by row.
+
+    For speed, each row is padded to a wider size than ``width``, so the true row width in
+    memory is ``plane.line_size`` (bytes), not ``width``. Copying as one straight stream
+    would skew the image, so we write only the first ``width`` columns of each row and
+    leave the padding untouched.
+
+    Args:
+        plane: Destination 16-bit plane.
+        src: Source image, shape ``(height, width)``, dtype ``uint16``.
+        fill_value: If given, every pixel (padding included) is set to this first, so the
+            padding holds clean data instead of garbage.
+    """
+    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 +121,7 @@ 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 +171,16 @@ 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 +195,18 @@ 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 +216,6 @@ 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/streaming_dataset.py

@@ -22,9 +22,11 @@ import numpy as np
 import torch
 from datasets import load_dataset
 
+from lerobot.configs import DEFAULT_DEPTH_UNIT, DepthEncoderConfig
 from lerobot.utils.constants import HF_LEROBOT_HOME, LOOKAHEAD_BACKTRACKTABLE, LOOKBACK_BACKTRACKTABLE
 
 from .dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
+from .depth_utils import dequantize_depth
 from .feature_utils import get_delta_indices
 from .io_utils import item_to_torch
 from .utils import (
@@ -35,6 +37,7 @@ from .utils import (
 )
 from .video_utils import (
     VideoDecoderCache,
+    decode_video_frames,
     decode_video_frames_torchcodec,
 )
 
@@ -252,6 +255,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         rng: np.random.Generator | None = None,
         shuffle: bool = True,
         return_uint8: bool = False,
+        depth_output_unit: str = DEFAULT_DEPTH_UNIT,
     ):
         """Initialize a StreamingLeRobotDataset.
 
@@ -272,6 +276,8 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
             seed (int, optional): Reproducibility random seed.
             rng (np.random.Generator | None, optional): Random number generator.
             shuffle (bool, optional): Whether to shuffle the dataset across exhaustions. Defaults to True.
+            depth_output_unit (str, optional): Physical unit depth maps are dequantized to ("m" or "mm").
+                Defaults to "mm".
         """
         super().__init__()
         self.repo_id = repo_id
@@ -290,6 +296,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         self.streaming = streaming
         self.buffer_size = buffer_size
         self._return_uint8 = return_uint8
+        self._depth_output_unit = depth_output_unit
 
         # We cache the video decoders to avoid re-initializing them at each frame (avoiding a ~10x slowdown)
         self.video_decoder_cache = None
@@ -306,6 +313,11 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         # Check version
         check_version_compatibility(self.repo_id, self.meta._version, CODEBASE_VERSION)
 
+        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
+        }
+
         self.delta_timestamps = None
         self.delta_indices = None
 
@@ -554,13 +566,34 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
         for video_key, query_ts in query_timestamps.items():
             root = self.meta.url_root if self.streaming and not self.streaming_from_local else self.root
             video_path = f"{root}/{self.meta.get_video_file_path(ep_idx, video_key)}"
-            frames = decode_video_frames_torchcodec(
+            if video_key in self.meta.depth_keys:
-                video_path,
+                # Depth maps are 12-bit quantized and only decodable via pyav; dequantize back
-                query_ts,
+                # to physical units to match the non-streaming reader.
-                self.tolerance_s,
+                frames = decode_video_frames(
-                decoder_cache=self.video_decoder_cache,
+                    video_path,
-                return_uint8=self._return_uint8,
+                    query_ts,
-            )
+                    self.tolerance_s,
+                    backend="pyav",
+                    return_uint8=False,
+                    is_depth=True,
+                )
+                depth_encoder = self._depth_encoder_configs[video_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,
+                )
+            else:
+                frames = decode_video_frames_torchcodec(
+                    video_path,
+                    query_ts,
+                    self.tolerance_s,
+                    decoder_cache=self.video_decoder_cache,
+                    return_uint8=self._return_uint8,
+                )
 
             item[video_key] = frames.squeeze(0) if len(query_ts) == 1 else frames
 

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 +39,17 @@ from datasets.features.features import register_feature
 from PIL import Image
 
 from lerobot.configs import (
+    DepthEncoderConfig,
+    RGBEncoderConfig,
     VideoEncoderConfig,
-    camera_encoder_defaults,
+    depth_encoder_defaults,
+    rgb_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 +59,7 @@ 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 +71,35 @@ 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.debug("Decoding depth maps is only supported with the 'pyav' backend, falling back to pyav.")
+        # 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 +110,7 @@ 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 +129,9 @@ 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).
@@ -132,7 +153,13 @@ def decode_video_frames_pyav(
     # https://pyav.basswood-io.com/docs/stable/api/container.html#av.container.InputContainer.seek
     with av.open(video_path) as container:
         stream = container.streams.video[0]
-        container.seek(int(first_ts * av.time_base), backward=True)
+        # Seek to the nearest keyframe at or before `first_ts` with a 1 frame margin
+        container.seek(
+            round(first_ts / stream.time_base) - 1,
+            backward=True,
+            any_frame=False,
+            stream=stream,
+        )
 
         for frame in container.decode(stream):
             if frame.pts is None:
@@ -140,9 +167,13 @@ 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 +216,7 @@ 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 +437,38 @@ 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:`rgb_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 = rgb_encoder_defaults()
+    vcodec = video_encoder.vcodec
+    pix_fmt = video_encoder.pix_fmt
 
     video_path = Path(video_path)
     imgs_dir = Path(imgs_dir)
@@ -428,17 +480,19 @@ 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 +509,19 @@ 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 +542,7 @@ 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 +554,7 @@ 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:`rgb_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 +562,7 @@ 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 rgb_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 +577,9 @@ 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 +761,21 @@ 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 +800,12 @@ 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 +813,29 @@ 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)
@@ -815,22 +893,27 @@ class StreamingVideoEncoder:
     def __init__(
         self,
         fps: int,
-        camera_encoder: VideoEncoderConfig | None = None,
+        rgb_encoder: RGBEncoderConfig | 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.
+            rgb_encoder: Video encoder settings applied to all RGB cameras.
-                When ``None``, :func:`camera_encoder_defaults` is used.
+                When ``None``, :func:`rgb_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._rgb_encoder = rgb_encoder or rgb_encoder_defaults()
+        self._depth_encoder = depth_encoder or depth_encoder_defaults()
         self._encoder_threads = encoder_threads
         self.queue_maxsize = queue_maxsize
 
@@ -843,18 +926,25 @@ 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 +953,15 @@ 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._rgb_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 +1168,23 @@ 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 +1202,10 @@ 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 +1214,18 @@ 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 +1286,13 @@ 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/hope_jr/hope_jr_arm.py

@@ -66,9 +66,14 @@ class HopeJrArm(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:
-        }
+            cfg = self.config.cameras[cam]
+            if getattr(cfg, "use_rgb", True):
+                features[cam] = (cfg.height, cfg.width, 3)
+            if getattr(cfg, "use_depth", False):
+                features[f"{cam}_depth"] = (cfg.height, cfg.width, 1)
+        return features
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -139,10 +144,17 @@ class HopeJrArm(Robot):
 
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
-            start = time.perf_counter()
+            if getattr(cam, "use_rgb", True):
-            obs_dict[cam_key] = cam.read_latest()
+                start = time.perf_counter()
-            dt_ms = (time.perf_counter() - start) * 1e3
+                obs_dict[cam_key] = cam.read_latest()
-            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+                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
 

src/lerobot/robots/hope_jr/hope_jr_hand.py

@@ -102,9 +102,14 @@ class HopeJrHand(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:
-        }
+            cfg = self.config.cameras[cam]
+            if getattr(cfg, "use_rgb", True):
+                features[cam] = (cfg.height, cfg.width, 3)
+            if getattr(cfg, "use_depth", False):
+                features[f"{cam}_depth"] = (cfg.height, cfg.width, 1)
+        return features
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -170,10 +175,17 @@ class HopeJrHand(Robot):
 
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
-            start = time.perf_counter()
+            if getattr(cam, "use_rgb", True):
-            obs_dict[cam_key] = cam.read_latest()
+                start = time.perf_counter()
-            dt_ms = (time.perf_counter() - start) * 1e3
+                obs_dict[cam_key] = cam.read_latest()
-            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+                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
 

src/lerobot/robots/koch_follower/koch_follower.py

@@ -68,9 +68,14 @@ class KochFollower(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:
-        }
+            cfg = self.config.cameras[cam]
+            if getattr(cfg, "use_rgb", True):
+                features[cam] = (cfg.height, cfg.width, 3)
+            if getattr(cfg, "use_depth", False):
+                features[f"{cam}_depth"] = (cfg.height, cfg.width, 1)
+        return features
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -192,10 +197,17 @@ class KochFollower(Robot):
 
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
-            start = time.perf_counter()
+            if getattr(cam, "use_rgb", True):
-            obs_dict[cam_key] = cam.read_latest()
+                start = time.perf_counter()
-            dt_ms = (time.perf_counter() - start) * 1e3
+                obs_dict[cam_key] = cam.read_latest()
-            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+                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
 

src/lerobot/robots/lekiwi/lekiwi.py

@@ -72,6 +72,12 @@ class LeKiwi(Robot):
         )
         self.arm_motors = [motor for motor in self.bus.motors if motor.startswith("arm")]
         self.base_motors = [motor for motor in self.bus.motors if motor.startswith("base")]
+        depth_cameras = [name for name, cfg in config.cameras.items() if getattr(cfg, "use_depth", False)]
+        if depth_cameras:
+            raise NotImplementedError(
+                f"Depth cameras are not supported on LeKiwi (got depth-enabled cameras: {depth_cameras}). "
+                "The host/client transport only carries color frames."
+            )
         self.cameras = make_cameras_from_configs(config.cameras)
 
     @property

src/lerobot/robots/lekiwi/lekiwi_client.py

@@ -44,6 +44,13 @@ class LeKiwiClient(Robot):
         self.id = config.id
         self.robot_type = config.type
 
+        depth_cameras = [name for name, cfg in config.cameras.items() if getattr(cfg, "use_depth", False)]
+        if depth_cameras:
+            raise NotImplementedError(
+                f"Depth cameras are not supported on LeKiwi (got depth-enabled cameras: {depth_cameras}). "
+                "The host/client transport only carries color frames."
+            )
+
         self.remote_ip = config.remote_ip
         self.port_zmq_cmd = config.port_zmq_cmd
         self.port_zmq_observations = config.port_zmq_observations

src/lerobot/robots/omx_follower/omx_follower.py

@@ -68,9 +68,14 @@ class OmxFollower(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:
-        }
+            cfg = self.config.cameras[cam]
+            if getattr(cfg, "use_rgb", True):
+                features[cam] = (cfg.height, cfg.width, 3)
+            if getattr(cfg, "use_depth", False):
+                features[f"{cam}_depth"] = (cfg.height, cfg.width, 1)
+        return features
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -175,10 +180,17 @@ class OmxFollower(Robot):
 
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
-            start = time.perf_counter()
+            if getattr(cam, "use_rgb", True):
-            obs_dict[cam_key] = cam.read_latest()
+                start = time.perf_counter()
-            dt_ms = (time.perf_counter() - start) * 1e3
+                obs_dict[cam_key] = cam.read_latest()
-            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+                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
 

src/lerobot/robots/openarm_follower/openarm_follower.py

@@ -101,9 +101,14 @@ class OpenArmFollower(Robot):
     @property
     def _cameras_ft(self) -> dict[str, tuple]:
         """Camera features for observation space."""
-        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:
-        }
+            cfg = self.config.cameras[cam]
+            if getattr(cfg, "use_rgb", True):
+                features[cam] = (cfg.height, cfg.width, 3)
+            if getattr(cfg, "use_depth", False):
+                features[f"{cam}_depth"] = (cfg.height, cfg.width, 1)
+        return features
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -242,10 +247,17 @@ class OpenArmFollower(Robot):
 
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
-            start = time.perf_counter()
+            if getattr(cam, "use_rgb", True):
-            obs_dict[cam_key] = cam.read_latest()
+                start = time.perf_counter()
-            dt_ms = (time.perf_counter() - start) * 1e3
+                obs_dict[cam_key] = cam.read_latest()
-            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+                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")
 
         dt_ms = (time.perf_counter() - start) * 1e3
         logger.debug(f"{self} get_observation took: {dt_ms:.1f}ms")

src/lerobot/robots/rebot_b601_follower/rebot_b601_follower.py

@@ -80,9 +80,14 @@ class RebotB601Follower(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:
-        }
+            cfg = self.config.cameras[cam]
+            if getattr(cfg, "use_rgb", True):
+                features[cam] = (cfg.height, cfg.width, 3)
+            if getattr(cfg, "use_depth", False):
+                features[f"{cam}_depth"] = (cfg.height, cfg.width, 1)
+        return features
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -213,10 +218,17 @@ class RebotB601Follower(Robot):
         logger.debug(f"{self} read state: {dt_ms:.1f}ms")
 
         for cam_key, cam in self.cameras.items():
-            start = time.perf_counter()
+            if getattr(cam, "use_rgb", True):
-            obs_dict[cam_key] = cam.read_latest()
+                start = time.perf_counter()
-            dt_ms = (time.perf_counter() - start) * 1e3
+                obs_dict[cam_key] = cam.read_latest()
-            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+                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
 

src/lerobot/robots/so_follower/so_follower.py

@@ -68,9 +68,13 @@ 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:
-        }
+            if getattr(self.cameras[cam], "use_rgb", True):
+                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]:
@@ -185,10 +189,17 @@ class SOFollower(Robot):
 
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
-            start = time.perf_counter()
+            if getattr(cam, "use_rgb", True):
-            obs_dict[cam_key] = cam.read_latest()
+                start = time.perf_counter()
-            dt_ms = (time.perf_counter() - start) * 1e3
+                obs_dict[cam_key] = cam.read_latest()
-            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+                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
 

src/lerobot/robots/unitree_g1/unitree_g1.py

@@ -222,9 +222,14 @@ class UnitreeG1(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:
-        }
+            cfg = self.config.cameras[cam]
+            if getattr(cfg, "use_rgb", True):
+                features[cam] = (cfg.height, cfg.width, 3)
+            if getattr(cfg, "use_depth", False):
+                features[f"{cam}_depth"] = (cfg.height, cfg.width, 1)
+        return features
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -458,7 +463,10 @@ class UnitreeG1(Robot):
 
         # Cameras - read images from ZMQ cameras
         for cam_name, cam in self._cameras.items():
-            obs[cam_name] = cam.read_latest()
+            if getattr(cam, "use_rgb", True):
+                obs[cam_name] = cam.read_latest()
+            if getattr(cam, "use_depth", False):
+                obs[f"{cam_name}_depth"] = cam.read_latest_depth()
 
         return obs
 

src/lerobot/rollout/context.py

@@ -332,7 +332,8 @@ def build_rollout_context(
                 cfg.dataset.repo_id,
                 root=cfg.dataset.root,
                 batch_encoding_size=cfg.dataset.video_encoding_batch_size,
-                camera_encoder=cfg.dataset.camera_encoder,
+                rgb_encoder=cfg.dataset.rgb_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,
@@ -367,7 +368,8 @@ def build_rollout_context(
                 image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera
                 * len(robot.cameras if hasattr(robot, "cameras") else []),
                 batch_encoding_size=cfg.dataset.video_encoding_batch_size,
-                camera_encoder=cfg.dataset.camera_encoder,
+                rgb_encoder=cfg.dataset.rgb_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_dataset_viz.py

@@ -77,15 +77,28 @@ from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD
 from lerobot.utils.utils import init_logging
 
 
+def check_chw_float32(frame: torch.Tensor) -> None:
+    """
+    Check if a frame is a channel-first, float32 tensor.
+    """
+    assert frame.dtype == torch.float32
+    assert frame.ndim == 3
+    c, h, w = frame.shape
+    assert c < h and c < w, f"expect channel first images, but instead {frame.shape}"
+
+
 def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
-    assert chw_float32_torch.dtype == torch.float32
+    check_chw_float32(chw_float32_torch)
-    assert chw_float32_torch.ndim == 3
-    c, h, w = chw_float32_torch.shape
-    assert c < h and c < w, f"expect channel first images, but instead {chw_float32_torch.shape}"
     hwc_uint8_numpy = (chw_float32_torch * 255).type(torch.uint8).permute(1, 2, 0).numpy()
     return hwc_uint8_numpy
 
 
+def to_hwc_uint16_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
+    check_chw_float32(chw_float32_torch)
+    hwc_uint16_numpy = chw_float32_torch.round().type(torch.uint16).permute(1, 2, 0).numpy()
+    return hwc_uint16_numpy
+
+
 def visualize_dataset(
     dataset: LeRobotDataset,
     episode_index: int,
@@ -138,6 +151,14 @@ def visualize_dataset(
 
     logging.info("Logging to Rerun")
 
+    # Use the dataset's q01/q99 depth statistics for robust depth range bounds
+    depth_ranges = {}
+    for key in dataset.meta.depth_keys:
+        stats = dataset.meta.stats[key]
+        lo = stats["q01"] if "q01" in stats else stats["min"]
+        hi = stats["q99"] if "q99" in stats else stats["max"]
+        depth_ranges[key] = (float(np.asarray(lo).item()), float(np.asarray(hi).item()))
+
     first_index = None
     for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
         if first_index is None:
@@ -149,9 +170,18 @@ def visualize_dataset(
 
             # display each camera image
             for key in dataset.meta.camera_keys:
-                img = to_hwc_uint8_numpy(batch[key][i])
+                if key in dataset.meta.depth_keys:
-                img_entity = rr.Image(img).compress() if display_compressed_images else rr.Image(img)
+                    depth = to_hwc_uint16_numpy(batch[key][i])
-                rr.log(key, entity=img_entity)
+                    depth_entity = rr.DepthImage(
+                        depth,
+                        colormap=rr.components.Colormap.Viridis,
+                        depth_range=depth_ranges[key],
+                    )
+                    rr.log(key, entity=depth_entity)
+                else:
+                    img = to_hwc_uint8_numpy(batch[key][i])
+                    img_entity = rr.Image(img).compress() if display_compressed_images else rr.Image(img)
+                    rr.log(key, entity=img_entity)
 
             # display each dimension of action space (e.g. actuators command)
             if ACTION in batch:

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 \
@@ -190,17 +199,17 @@ Re-encode all videos in a dataset (saves to lerobot/pusht_reencoded by default):
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type reencode_videos \
-        --operation.camera_encoder.vcodec h264 \
+        --operation.rgb_encoder.vcodec h264 \
-        --operation.camera_encoder.pix_fmt yuv420p \
+        --operation.rgb_encoder.pix_fmt yuv420p \
-        --operation.camera_encoder.crf 23
+        --operation.rgb_encoder.crf 23
 
 Re-encode videos into a new dataset using 4 parallel processes:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --new_repo_id lerobot/pusht_h264 \
         --operation.type reencode_videos \
-        --operation.camera_encoder.vcodec h264 \
+        --operation.rgb_encoder.vcodec h264 \
-        --operation.camera_encoder.crf 23 \
+        --operation.rgb_encoder.crf 23 \
         --operation.num_workers 4
 
 Re-encode videos in-place (overwrites original dataset):
@@ -208,9 +217,16 @@ Re-encode videos in-place (overwrites original dataset):
         --repo_id lerobot/pusht \
         --new_repo_id lerobot/pusht \
         --operation.type reencode_videos \
-        --operation.camera_encoder.vcodec h264 \
+        --operation.rgb_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.rgb_encoder.vcodec h264 \
+        --operation.depth_encoder.extra_options '{"x265-params": "lossless=1"}'
+
 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,
+    RGBEncoderConfig,
+    depth_encoder_defaults,
+    parser,
+    rgb_encoder_defaults,
+)
 from lerobot.datasets import (
     LeRobotDataset,
     convert_image_to_video_dataset,
@@ -287,7 +309,8 @@ class ModifyTasksConfig(OperationConfig):
 @dataclass
 class ConvertImageToVideoConfig(OperationConfig):
     output_dir: str | None = None
-    camera_encoder: VideoEncoderConfig = field(default_factory=camera_encoder_defaults)
+    rgb_encoder: RGBEncoderConfig = field(default_factory=rgb_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
@@ -308,7 +331,8 @@ class RecomputeStatsConfig(OperationConfig):
 @OperationConfig.register_subclass("reencode_videos")
 @dataclass
 class ReencodeVideosConfig(OperationConfig):
-    camera_encoder: VideoEncoderConfig = field(default_factory=camera_encoder_defaults)
+    rgb_encoder: RGBEncoderConfig = field(default_factory=rgb_encoder_defaults)
+    depth_encoder: DepthEncoderConfig = field(default_factory=depth_encoder_defaults)
     num_workers: int = 0
     encoder_threads: int | None = None
     overwrite: bool = False
@@ -601,7 +625,8 @@ def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
         dataset=dataset,
         output_dir=output_dir,
         repo_id=output_repo_id,
-        camera_encoder=getattr(cfg.operation, "camera_encoder", None) or camera_encoder_defaults(),
+        rgb_encoder=getattr(cfg.operation, "rgb_encoder", None) or rgb_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.rgb_encoder} "
+        f"and depth encoder {cfg.operation.depth_encoder}"
+    )
     reencode_dataset(
         dataset,
-        camera_encoder=cfg.operation.camera_encoder,
+        rgb_encoder=cfg.operation.rgb_encoder,
+        depth_encoder=cfg.operation.depth_encoder,
         encoder_threads=cfg.operation.encoder_threads,
         num_workers=cfg.operation.num_workers,
     )

src/lerobot/scripts/lerobot_record.py

@@ -79,9 +79,9 @@ lerobot-record \\
     --dataset.single_task="Grab the cube" \\
     --dataset.streaming_encoding=true \\
     --dataset.encoder_threads=2 \\
-    --dataset.camera_encoder.vcodec=h264 \\
+    --dataset.rgb_encoder.vcodec=h264 \\
-    --dataset.camera_encoder.preset=fast \\
+    --dataset.rgb_encoder.preset=fast \\
-    --dataset.camera_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2} \\
+    --dataset.rgb_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2} \\
     --display_data=true
 ```
 """
@@ -400,7 +400,8 @@ def record(
                 cfg.dataset.repo_id,
                 root=cfg.dataset.root,
                 batch_encoding_size=cfg.dataset.video_encoding_batch_size,
-                camera_encoder=cfg.dataset.camera_encoder,
+                rgb_encoder=cfg.dataset.rgb_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,
@@ -429,7 +430,8 @@ def record(
                 image_writer_processes=cfg.dataset.num_image_writer_processes,
                 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,
+                rgb_encoder=cfg.dataset.rgb_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,
@@ -443,7 +445,7 @@ def record(
 
         if not cfg.dataset.streaming_encoding:
             logging.info(
-                "Streaming encoding is disabled. If you have capable hardware, consider enabling it for way faster episode saving. --dataset.streaming_encoding=true --dataset.encoder_threads=2 # --dataset.camera_encoder.vcodec=auto. More info in the documentation: https://huggingface.co/docs/lerobot/streaming_video_encoding"
+                "Streaming encoding is disabled. If you have capable hardware, consider enabling it for way faster episode saving. --dataset.streaming_encoding=true --dataset.encoder_threads=2 # --dataset.rgb_encoder.vcodec=auto. More info in the documentation: https://huggingface.co/docs/lerobot/streaming_video_encoding"
             )
 
         with VideoEncodingManager(dataset):

src/lerobot/scripts/lerobot_rollout.py

@@ -142,9 +142,9 @@ Usage examples
         --robot.port=/dev/ttyACM0 \\
         --task="pick up cube" --duration=60 \\
         --display_data=true \\
-        --dataset.camera_encoder.vcodec=h264 \\
+        --dataset.rgb_encoder.vcodec=h264 \\
-        --dataset.camera_encoder.preset=fast \\
+        --dataset.rgb_encoder.preset=fast \\
-        --dataset.camera_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2}
+        --dataset.rgb_encoder.extra_options={"tune": "film", "profile:v": "high", "bf": 2}
 """
 
 import logging

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 +107,10 @@ 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)
+                    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

@@ -47,6 +47,7 @@ class _FakeMeta:
     def __init__(self, video_keys: list[str], image_keys: list[str], video_path: Path | None = None) -> None:
         self.video_keys = video_keys
         self.camera_keys = [*video_keys, *image_keys]
+        self.depth_keys = []
         self._video_path = video_path
         self.episodes = {0: {f"videos/{key}/from_timestamp": 0.0 for key in video_keys}}
 
@@ -208,14 +209,14 @@ 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/datasets/test_aggregate.py

@@ -29,7 +29,10 @@ 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):
@@ -211,6 +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.
 
@@ -260,7 +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
@@ -272,14 +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],
@@ -306,6 +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)
 
 
@@ -423,7 +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
 
@@ -432,14 +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],
@@ -470,6 +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
@@ -489,7 +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):
@@ -498,9 +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],
@@ -517,12 +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):
@@ -639,25 +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(
@@ -692,6 +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_compute_stats.py

@@ -35,7 +35,11 @@ from lerobot.utils.constants import OBS_IMAGE, OBS_STATE
 
 
 def mock_load_image_as_numpy(path, dtype, channel_first):
-    return np.ones((3, 32, 32), dtype=dtype) if channel_first else np.ones((32, 32, 3), dtype=dtype)
+    is_depth = "depth" in str(path)
+    channels = 1 if is_depth else 3
+    out_dtype = np.uint16 if is_depth else dtype
+    arr = np.arange(channels * 32 * 32, dtype=out_dtype).reshape(channels, 32, 32)
+    return arr if channel_first else arr.transpose(1, 2, 0)
 
 
 @pytest.fixture
@@ -168,22 +172,33 @@ def test_get_feature_stats_single_value():
 
 
 def test_compute_episode_stats():
+    depth_key = "observation.images.depth"
     episode_data = {
         OBS_IMAGE: [f"image_{i}.jpg" for i in range(100)],
+        depth_key: [f"depth_{i}.tiff" for i in range(100)],
         OBS_STATE: np.random.rand(100, 10),
     }
     features = {
         OBS_IMAGE: {"dtype": "image"},
+        depth_key: {"dtype": "image", "info": {"is_depth_map": True}},
         OBS_STATE: {"dtype": "numeric"},
     }
 
     with patch("lerobot.datasets.compute_stats.load_image_as_numpy", side_effect=mock_load_image_as_numpy):
         stats = compute_episode_stats(episode_data, features)
 
-    assert OBS_IMAGE in stats and OBS_STATE in stats
+    assert OBS_IMAGE in stats and depth_key in stats and OBS_STATE in stats
     assert stats[OBS_IMAGE]["count"].item() == 100
+    assert stats[depth_key]["count"].item() == 100
     assert stats[OBS_STATE]["count"].item() == 100
     assert stats[OBS_IMAGE]["mean"].shape == (3, 1, 1)
+    assert stats[depth_key]["mean"].shape == (1, 1, 1)
+    # Depth keeps raw values: max far exceeds 255, proving no /255 and no uint8 downcast.
+    assert stats[depth_key]["min"].item() == 0.0
+    assert stats[depth_key]["max"].item() == 1023.0
+    # RGB is normalized to [0, 1].
+    np.testing.assert_allclose(stats[OBS_IMAGE]["min"], 0.0)
+    np.testing.assert_allclose(stats[OBS_IMAGE]["max"], 1.0)
 
 
 def test_assert_type_and_shape_valid():
@@ -618,25 +633,31 @@ def test_compute_episode_stats_with_custom_quantiles():
 def test_compute_episode_stats_with_image_data():
     """Test quantile computation with image features."""
     image_paths = [f"image_{i}.jpg" for i in range(50)]
+    depth_paths = [f"depth_{i}.tiff" for i in range(50)]
     episode_data = {
         "observation.image": image_paths,
+        "observation.images.depth": depth_paths,
         "action": np.random.normal(0, 1, (50, 5)),
     }
     features = {
         "observation.image": {"dtype": "image"},
+        "observation.images.depth": {"dtype": "image", "info": {"is_depth_map": True}},
         "action": {"dtype": "float32", "shape": (5,)},
     }
 
     with patch("lerobot.datasets.compute_stats.load_image_as_numpy", side_effect=mock_load_image_as_numpy):
         stats = compute_episode_stats(episode_data, features)
 
-    # Image quantiles should be normalized and have correct shape
+    # RGB image quantiles should be normalized and per-channel.
-    assert "q01" in stats["observation.image"]
+    for q in ("q01", "q50", "q99"):
-    assert "q50" in stats["observation.image"]
+        assert stats["observation.image"][q].shape == (3, 1, 1)
-    assert "q99" in stats["observation.image"]
+
-    assert stats["observation.image"]["q01"].shape == (3, 1, 1)
+    # Depth quantiles are single-channel and kept in raw (un-normalized) units.
-    assert stats["observation.image"]["q50"].shape == (3, 1, 1)
+    for q in ("q01", "q50", "q99"):
-    assert stats["observation.image"]["q99"].shape == (3, 1, 1)
+        assert stats["observation.images.depth"][q].shape == (1, 1, 1)
+    # Depth max stays in raw units (not /255, not uint8-capped); RGB is normalized.
+    assert stats["observation.images.depth"]["max"].item() == 1023.0
+    np.testing.assert_allclose(stats["observation.image"]["max"], 1.0)
 
     # Action quantiles should have correct shape
     assert stats["action"]["q01"].shape == (5,)

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, RGBEncoderConfig
 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
@@ -1251,7 +1253,7 @@ def test_convert_image_to_video_dataset(tmp_path):
             dataset=source_dataset,
             output_dir=output_dir,
             repo_id="lerobot/pusht_video",
-            camera_encoder=VideoEncoderConfig(
+            rgb_encoder=RGBEncoderConfig(
                 vcodec="libsvtav1",
                 pix_fmt="yuv420p",
                 g=2,
@@ -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",
+            rgb_encoder=RGBEncoderConfig(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(
@@ -1342,29 +1466,29 @@ def test_reencode_dataset_multi_key_multiprocessing(
 ):
     """Re-encode a two-camera dataset with num_workers=2 and verify metadata refresh."""
     features = features_factory(use_videos=True)
-    initial_cfg = VideoEncoderConfig(vcodec="libsvtav1", g=2, crf=30, preset=12)
+    initial_cfg = RGBEncoderConfig(vcodec="libsvtav1", g=2, crf=30, preset=12)
     dataset = empty_lerobot_dataset_factory(
         root=tmp_path / "ds",
         features=features,
         use_videos=True,
-        camera_encoder=initial_cfg,
+        rgb_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()
 
     assert len(dataset.meta.video_keys) == 2
 
-    target_cfg = VideoEncoderConfig(vcodec="h264", g=6, crf=23, pix_fmt="yuv420p")
+    target_cfg = RGBEncoderConfig(vcodec="h264", g=6, crf=23, pix_fmt="yuv420p")
 
-    result = reencode_dataset(dataset, camera_encoder=target_cfg, num_workers=2)
+    result = reencode_dataset(dataset, rgb_encoder=target_cfg, num_workers=2)
 
     assert result is dataset
 
     persisted_info = load_info(dataset.root)
     for vk in dataset.meta.video_keys:
-        persisted_encoder = VideoEncoderConfig.from_video_info(persisted_info.features[vk].get("info", {}))
+        persisted_encoder = RGBEncoderConfig.from_video_info(persisted_info.features[vk].get("info", {}))
         assert persisted_encoder == target_cfg

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

@@ -1534,6 +1534,10 @@ def test_valid_video_codecs_constant():
     assert "auto" in VALID_VIDEO_CODECS
     assert "h264_videotoolbox" in VALID_VIDEO_CODECS
     assert "h264_nvenc" in VALID_VIDEO_CODECS
+    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) == 10
 
 

tests/datasets/test_depth.py

@@ -0,0 +1,247 @@
+"""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_METER_UNIT,
+    DEPTH_MILLIMETER_UNIT,
+    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", [DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT])
+    @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 == DEPTH_MILLIMETER_UNIT:
+            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", [DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT])
+    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 == DEPTH_METER_UNIT 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=DEPTH_METER_UNIT, 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=DEPTH_METER_UNIT, 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)
 
 
@@ -344,7 +344,7 @@ def test_with_different_image_formats(tmp_path, img_array_factory):
     writer = AsyncImageWriter()
     try:
         image_array = img_array_factory()
-        formats = ["png", "jpeg", "bmp"]
+        formats = ["png", "tiff", "tif"]
         for fmt in formats:
             fpath = tmp_path / f"test_image.{fmt}"
             write_image(image_array, fpath)

tests/datasets/test_streaming_video_encoder.py

@@ -26,7 +26,7 @@ pytest.importorskip("av", reason="av is required (install lerobot[dataset])")
 
 import av  # noqa: E402
 
-from lerobot.configs import VideoEncoderConfig
+from lerobot.configs import RGBEncoderConfig
 from lerobot.datasets.pyav_utils import get_codec
 from lerobot.datasets.video_utils import (
     StreamingVideoEncoder,
@@ -57,13 +57,11 @@ class TestCameraEncoderThread:
         result_queue: queue.Queue = queue.Queue(maxsize=1)
         stop_event = threading.Event()
 
-        enc_cfg = VideoEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13)
+        enc_cfg = RGBEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13)
         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,
@@ -108,13 +106,11 @@ class TestCameraEncoderThread:
         result_queue: queue.Queue = queue.Queue(maxsize=1)
         stop_event = threading.Event()
 
-        enc_cfg = VideoEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13)
+        enc_cfg = RGBEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13)
         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,
@@ -142,13 +138,11 @@ class TestCameraEncoderThread:
         result_queue: queue.Queue = queue.Queue(maxsize=1)
         stop_event = threading.Event()
 
-        enc_cfg = VideoEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13)
+        enc_cfg = RGBEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13)
         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,
@@ -171,15 +165,15 @@ class TestCameraEncoderThread:
 
 class TestStreamingVideoEncoder:
     def _make_encoder_config(self, **kwargs):
-        """Helper to build a VideoEncoderConfig."""
+        """Helper to build an RGBEncoderConfig."""
-        return VideoEncoderConfig(**kwargs)
+        return RGBEncoderConfig(**kwargs)
 
     def test_single_camera_episode(self, tmp_path):
         """Test encoding a single camera episode."""
         video_keys = [f"{OBS_IMAGES}.laptop"]
         encoder = StreamingVideoEncoder(
             fps=30,
-            camera_encoder=self._make_encoder_config(
+            rgb_encoder=self._make_encoder_config(
                 vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13
             ),
         )
@@ -211,7 +205,7 @@ class TestStreamingVideoEncoder:
         video_keys = [f"{OBS_IMAGES}.laptop", f"{OBS_IMAGES}.phone"]
         encoder = StreamingVideoEncoder(
             fps=30,
-            camera_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
+            rgb_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
         )
         encoder.start_episode(video_keys, tmp_path)
 
@@ -237,7 +231,7 @@ class TestStreamingVideoEncoder:
         video_keys = [f"{OBS_IMAGES}.cam"]
         encoder = StreamingVideoEncoder(
             fps=30,
-            camera_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
+            rgb_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
         )
 
         for ep in range(3):
@@ -263,7 +257,7 @@ class TestStreamingVideoEncoder:
         video_keys = [f"{OBS_IMAGES}.cam"]
         encoder = StreamingVideoEncoder(
             fps=30,
-            camera_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
+            rgb_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
         )
 
         encoder.start_episode(video_keys, tmp_path)
@@ -309,7 +303,7 @@ class TestStreamingVideoEncoder:
         video_keys = [f"{OBS_IMAGES}.cam"]
         encoder = StreamingVideoEncoder(
             fps=30,
-            camera_encoder=self._make_encoder_config(
+            rgb_encoder=self._make_encoder_config(
                 vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13
             ),
         )
@@ -346,7 +340,7 @@ class TestStreamingVideoEncoder:
         video_keys = [f"{OBS_IMAGES}.cam1", f"{OBS_IMAGES}.cam2"]
         encoder = StreamingVideoEncoder(
             fps=30,
-            camera_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
+            rgb_encoder=self._make_encoder_config(vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30),
         )
         encoder.start_episode(video_keys, tmp_path)
 
@@ -375,7 +369,7 @@ class TestStreamingVideoEncoder:
     def test_encoder_threads_passed_to_thread(self, tmp_path):
         """Test that encoder_threads is stored and passed through to encoder threads."""
         video_keys = [f"{OBS_IMAGES}.cam"]
-        cfg = VideoEncoderConfig(
+        cfg = RGBEncoderConfig(
             vcodec="libsvtav1",
             pix_fmt="yuv420p",
             g=2,
@@ -383,7 +377,7 @@ class TestStreamingVideoEncoder:
         )
         encoder = StreamingVideoEncoder(
             fps=30,
-            camera_encoder=cfg,
+            rgb_encoder=cfg,
             encoder_threads=2,
         )
         assert encoder._encoder_threads == 2
@@ -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
@@ -422,7 +417,7 @@ class TestStreamingVideoEncoder:
         video_keys = [f"{OBS_IMAGES}.cam"]
         encoder = StreamingVideoEncoder(
             fps=30,
-            camera_encoder=self._make_encoder_config(
+            rgb_encoder=self._make_encoder_config(
                 vcodec="libsvtav1", pix_fmt="yuv420p", g=2, crf=30, preset=13
             ),
             queue_maxsize=1,

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, RGBEncoderConfig, 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
@@ -48,19 +56,74 @@ 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")
 
 
-# ─── VideoEncoderConfig / codec options ──────────────────────────────
+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"]
+
+
+# ─── RGBEncoderConfig / codec options ──────────────────────────────
 
 
 class TestCodecOptions:
     @require_libsvtav1
     def test_libsvtav1_defaults(self):
-        cfg = VideoEncoderConfig()
+        cfg = RGBEncoderConfig()
         opts = cfg.get_codec_options()
         assert opts["g"] == 2
         assert opts["crf"] == 30
@@ -68,12 +131,12 @@ class TestCodecOptions:
 
     @require_libsvtav1
     def test_libsvtav1_custom_preset(self):
-        cfg = VideoEncoderConfig(preset=8)
+        cfg = RGBEncoderConfig(preset=8)
         assert cfg.get_codec_options()["preset"] == 8
 
     @require_h264
     def test_h264_options(self):
-        cfg = VideoEncoderConfig(vcodec="h264", g=10, crf=23, preset=None)
+        cfg = RGBEncoderConfig(vcodec="h264", g=10, crf=23, preset=None)
         opts = cfg.get_codec_options()
         assert opts["g"] == 10
         assert opts["crf"] == 23
@@ -81,120 +144,120 @@ class TestCodecOptions:
 
     @require_videotoolbox
     def test_videotoolbox_options(self):
-        cfg = VideoEncoderConfig(vcodec="h264_videotoolbox", g=2, crf=30, preset=None)
+        cfg = RGBEncoderConfig(vcodec="h264_videotoolbox", g=2, crf=30, preset=None)
         opts = cfg.get_codec_options()
         assert opts["g"] == 2
         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)
+        cfg = RGBEncoderConfig(vcodec="h264_nvenc", g=2, crf=25, preset=None)
         opts = cfg.get_codec_options()
         assert opts["rc"] == 0
         assert opts["qp"] == 25
         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)
+        cfg = RGBEncoderConfig(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)
+        cfg = RGBEncoderConfig(vcodec="h264_qsv", crf=25, preset=None)
         assert cfg.get_codec_options()["global_quality"] == 25
 
     @require_h264
     def test_no_g_no_crf(self):
-        cfg = VideoEncoderConfig(vcodec="h264", g=None, crf=None, preset=None)
+        cfg = RGBEncoderConfig(vcodec="h264", g=None, crf=None, preset=None)
         opts = cfg.get_codec_options()
         assert "g" not in opts
         assert "crf" not in opts
 
     @require_libsvtav1
     def test_encoder_threads_libsvtav1(self):
-        cfg = VideoEncoderConfig(fast_decode=0)
+        cfg = RGBEncoderConfig(fast_decode=0)
         opts = cfg.get_codec_options(encoder_threads=4)
         assert "lp=4" in opts.get("svtav1-params", "")
 
     @require_h264
     def test_encoder_threads_h264(self):
-        cfg = VideoEncoderConfig(vcodec="h264", preset=None)
+        cfg = RGBEncoderConfig(vcodec="h264", preset=None)
         assert cfg.get_codec_options(encoder_threads=2)["threads"] == 2
 
     @require_libsvtav1
     def test_fast_decode_libsvtav1(self):
-        cfg = VideoEncoderConfig(fast_decode=1)
+        cfg = RGBEncoderConfig(fast_decode=1)
         opts = cfg.get_codec_options()
         assert "fast-decode=1" in opts.get("svtav1-params", "")
 
     @require_libsvtav1
     def test_libsvtav1_fast_decode_clamped_to_svt_range(self):
         """Out-of-range fast_decode is clamped to [0, 2] in svtav1-params (SVT-AV1 FastDecode)."""
-        cfg = VideoEncoderConfig(fast_decode=100)
+        cfg = RGBEncoderConfig(fast_decode=100)
         assert "fast-decode=2" in cfg.get_codec_options().get("svtav1-params", "")
-        cfg_neg = VideoEncoderConfig(fast_decode=-5)
+        cfg_neg = RGBEncoderConfig(fast_decode=-5)
         assert "fast-decode=0" in cfg_neg.get_codec_options().get("svtav1-params", "")
 
     @require_h264
     def test_fast_decode_h264(self):
-        cfg = VideoEncoderConfig(vcodec="h264", fast_decode=1, preset=None)
+        cfg = RGBEncoderConfig(vcodec="h264", fast_decode=1, preset=None)
         assert cfg.get_codec_options()["tune"] == "fastdecode"
 
     @require_libsvtav1
     def test_pix_fmt_unsupported_raises(self):
         """Passing an unsupported pix_fmt is a hard error."""
         with pytest.raises(ValueError, match="pix_fmt"):
-            VideoEncoderConfig(pix_fmt="yuv444p")  # libsvtav1 only supports yuv420p variants
+            RGBEncoderConfig(pix_fmt="yuv444p")  # libsvtav1 only supports yuv420p variants
 
     @require_libsvtav1
     @require_h264
     def test_preset_default_behaviour(self):
         """Empty constructor picks preset=12 (libsvtav1 path); other codecs stay None."""
-        assert VideoEncoderConfig().preset == 12
+        assert RGBEncoderConfig().preset == 12
-        assert VideoEncoderConfig(vcodec="libsvtav1").preset == 12
+        assert RGBEncoderConfig(vcodec="libsvtav1").preset == 12
-        assert VideoEncoderConfig(vcodec="h264").preset is None
+        assert RGBEncoderConfig(vcodec="h264").preset is None
-        assert VideoEncoderConfig(vcodec="h264", preset=None).preset is None
+        assert RGBEncoderConfig(vcodec="h264", preset=None).preset is None
 
     @require_h264
     def test_preset_string_on_h264(self):
         """h264 accepts string presets and forwards them to FFmpeg."""
-        cfg = VideoEncoderConfig(vcodec="h264", preset="slow")
+        cfg = RGBEncoderConfig(vcodec="h264", preset="slow")
         assert cfg.get_codec_options()["preset"] == "slow"
 
     @require_videotoolbox
     def test_preset_on_videotoolbox_not_set(self):
         """videotoolbox has no preset option at all."""
-        cfg = VideoEncoderConfig(vcodec="h264_videotoolbox", preset="slow")
+        cfg = RGBEncoderConfig(vcodec="h264_videotoolbox", preset="slow")
         assert "preset" not in cfg.get_codec_options()
 
     @require_libsvtav1
     def test_libsvtav1_preset_out_of_range_raises(self):
         """libsvtav1 preset must sit in [-2, 13] as exposed by PyAV."""
         with pytest.raises(ValueError, match="out of range"):
-            VideoEncoderConfig(vcodec="libsvtav1", preset=100)
+            RGBEncoderConfig(vcodec="libsvtav1", preset=100)
         with pytest.raises(ValueError, match="out of range"):
-            VideoEncoderConfig(vcodec="libsvtav1", preset=-3)
+            RGBEncoderConfig(vcodec="libsvtav1", preset=-3)
 
     @require_libsvtav1
     def test_libsvtav1_crf_out_of_range_raises(self):
         """libsvtav1 crf must sit in [0, 63]."""
         with pytest.raises(ValueError, match="crf.*out of range"):
-            VideoEncoderConfig(vcodec="libsvtav1", crf=64)
+            RGBEncoderConfig(vcodec="libsvtav1", crf=64)
 
     @require_libsvtav1
     def test_libsvtav1_crf_rejects_python_float(self):
         """libsvtav1 exposes ``crf`` as an INT AVOption; Python float must not pass validation."""
         with pytest.raises(ValueError, match="float values are not allowed"):
-            VideoEncoderConfig(vcodec="libsvtav1", crf=2.5)
+            RGBEncoderConfig(vcodec="libsvtav1", crf=2.5)
 
     @require_libsvtav1
     def test_libsvtav1_extra_crf_rejects_fractional_string(self):
         """INT options reject fractional values even when supplied only via ``extra_options``."""
         with pytest.raises(ValueError, match="float values are not allowed"):
-            VideoEncoderConfig(
+            RGBEncoderConfig(
                 vcodec="libsvtav1",
                 crf=None,
                 extra_options={"crf": "2.5"},
@@ -203,7 +266,7 @@ class TestCodecOptions:
     @require_libsvtav1
     def test_libsvtav1_extra_crf_rejects_float(self):
         with pytest.raises(ValueError, match="float values are not allowed"):
-            VideoEncoderConfig(
+            RGBEncoderConfig(
                 vcodec="libsvtav1",
                 crf=None,
                 extra_options={"crf": 2.5},
@@ -212,13 +275,13 @@ class TestCodecOptions:
     @require_h264
     def test_h264_crf_accepts_float_and_int(self):
         """x264 exposes crf as a FLOAT option, so both int and float are accepted."""
-        assert VideoEncoderConfig(vcodec="h264", crf=23).get_codec_options()["crf"] == 23
+        assert RGBEncoderConfig(vcodec="h264", crf=23).get_codec_options()["crf"] == 23
-        assert VideoEncoderConfig(vcodec="h264", crf=23.5).get_codec_options()["crf"] == 23.5
+        assert RGBEncoderConfig(vcodec="h264", crf=23.5).get_codec_options()["crf"] == 23.5
 
     @require_libsvtav1
     def test_validate_is_rerunnable(self):
         """After mutating a field, validate() re-checks and surfaces new issues."""
-        cfg = VideoEncoderConfig(vcodec="libsvtav1")
+        cfg = RGBEncoderConfig(vcodec="libsvtav1")
         cfg.preset = 100  # now out of range
         with pytest.raises(ValueError, match="out of range"):
             cfg.validate()
@@ -227,58 +290,58 @@ class TestCodecOptions:
 class TestExtraOptions:
     @require_libsvtav1
     def test_default_is_empty_dict(self):
-        cfg = VideoEncoderConfig()
+        cfg = RGBEncoderConfig()
         assert cfg.extra_options == {}
 
     @require_libsvtav1
     def test_unknown_key_passes_through(self):
         """Keys not published as AVOptions are forwarded to FFmpeg."""
-        cfg = VideoEncoderConfig(extra_options={"totally_made_up_option": "value"})
+        cfg = RGBEncoderConfig(extra_options={"totally_made_up_option": "value"})
         assert cfg.extra_options == {"totally_made_up_option": "value"}
 
     @require_libsvtav1
     def test_numeric_value_in_range_ok(self):
         """libsvtav1 exposes ``qp`` as INT in [0, 63]."""
-        cfg = VideoEncoderConfig(extra_options={"qp": 30})
+        cfg = RGBEncoderConfig(extra_options={"qp": 30})
         assert cfg.extra_options == {"qp": 30}
 
     @require_libsvtav1
     def test_numeric_out_of_range_raises(self):
         with pytest.raises(ValueError, match=r"qp=.*out of range"):
-            VideoEncoderConfig(extra_options={"qp": 999})
+            RGBEncoderConfig(extra_options={"qp": 999})
 
     @require_libsvtav1
     def test_numeric_string_accepted_in_range(self):
         """Numeric strings are accepted for numeric options (mirrors FFmpeg)."""
-        cfg = VideoEncoderConfig(extra_options={"qp": "18"})
+        cfg = RGBEncoderConfig(extra_options={"qp": "18"})
         assert cfg.extra_options == {"qp": "18"}
 
     @require_libsvtav1
     def test_numeric_string_out_of_range_raises(self):
         with pytest.raises(ValueError, match=r"qp=.*out of range"):
-            VideoEncoderConfig(extra_options={"qp": "999"})
+            RGBEncoderConfig(extra_options={"qp": "999"})
 
     @require_libsvtav1
     def test_non_numeric_string_on_numeric_option_raises(self):
         with pytest.raises(ValueError, match=r"qp=.*not numeric"):
-            VideoEncoderConfig(extra_options={"qp": "medium"})
+            RGBEncoderConfig(extra_options={"qp": "medium"})
 
     @require_libsvtav1
     def test_bool_on_numeric_option_raises(self):
         """``bool`` is explicitly rejected for numeric options."""
         with pytest.raises(ValueError, match=r"qp=.*not numeric"):
-            VideoEncoderConfig(extra_options={"qp": True})
+            RGBEncoderConfig(extra_options={"qp": True})
 
     @require_h264
     def test_string_option_passes_through_unchecked(self):
         """String-typed AVOptions are NOT enum-checked (too many accept freeform)."""
-        cfg = VideoEncoderConfig(vcodec="h264", preset=None, extra_options={"tune": "some-future-tune"})
+        cfg = RGBEncoderConfig(vcodec="h264", preset=None, extra_options={"tune": "some-future-tune"})
         assert cfg.extra_options == {"tune": "some-future-tune"}
 
     @require_libsvtav1
     def test_merged_into_codec_options_and_stringified(self):
         """Typed merge by default; ``as_strings=True`` matches FFmpeg option dict."""
-        cfg = VideoEncoderConfig(extra_options={"qp": 20})
+        cfg = RGBEncoderConfig(extra_options={"qp": 20})
         opts = cfg.get_codec_options()
         assert opts["qp"] == 20
         assert isinstance(opts["qp"], int)
@@ -287,25 +350,25 @@ class TestExtraOptions:
     @require_libsvtav1
     def test_structured_fields_win_on_collision(self):
         """A colliding extra_options key is discarded; the structured field wins."""
-        cfg = VideoEncoderConfig(crf=30, extra_options={"crf": 18})
+        cfg = RGBEncoderConfig(crf=30, extra_options={"crf": 18})
         assert cfg.get_codec_options()["crf"] == 30
 
 
 class TestEncoderDetection:
     @require_h264
     def test_explicit_codec_kept_when_available(self):
-        cfg = VideoEncoderConfig(vcodec="h264")
+        cfg = RGBEncoderConfig(vcodec="h264")
         assert cfg.vcodec == "h264"
 
     @require_videotoolbox
     def test_auto_picks_videotoolbox_when_available(self):
         """``h264_videotoolbox`` sits at the top of ``HW_VIDEO_CODECS`` so it wins when present."""
-        cfg = VideoEncoderConfig(vcodec="auto")
+        cfg = RGBEncoderConfig(vcodec="auto")
         assert cfg.vcodec == "h264_videotoolbox"
 
     def test_invalid_codec_raises(self):
         with pytest.raises(ValueError, match="Invalid vcodec"):
-            VideoEncoderConfig(vcodec="not_a_real_codec")
+            RGBEncoderConfig(vcodec="not_a_real_codec")
 
     def test_hw_encoder_names_listed_as_valid(self):
         assert "auto" in VALID_VIDEO_CODECS
@@ -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
@@ -383,9 +393,9 @@ class TestGetVideoInfo:
 
     @require_libsvtav1
     def test_merges_encoder_config_as_video_prefixed_entries(self):
-        cfg = VideoEncoderConfig(vcodec="libsvtav1", g=2, crf=30, preset=12)
+        cfg = RGBEncoderConfig(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
@@ -396,13 +406,18 @@ class TestGetVideoInfo:
 
     @require_libsvtav1
     def test_stream_derived_keys_take_precedence_over_config(self):
-        cfg = VideoEncoderConfig(vcodec="libsvtav1", pix_fmt="yuv420p")
+        cfg = RGBEncoderConfig(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")
 
@@ -458,10 +473,10 @@ class TestEncodeVideoFrames:
     @require_libsvtav1
     def test_custom_encoder_config_fields_stored_in_info(self, tmp_path):
         """All stream-derived and encoder config fields are present after encoding."""
-        cfg = VideoEncoderConfig(vcodec="libsvtav1", g=4, crf=25, preset=10)
+        cfg = RGBEncoderConfig(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
@@ -487,15 +502,15 @@ class TestReencodeVideo:
     def test_reencode_video(self, tmp_path):
         src = TEST_ARTIFACTS_DIR / "clip_4frames.mp4"
         out = tmp_path / "reencoded.mp4"
-        cfg = VideoEncoderConfig(vcodec="h264", g=6, crf=23, pix_fmt="yuv444p")
+        cfg = RGBEncoderConfig(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
@@ -508,8 +523,8 @@ class TestReencodeVideo:
     def test_reencode_video_trim_window(self, tmp_path):
         src = TEST_ARTIFACTS_DIR / "clip_6frames.mp4"
         out = tmp_path / "trim_window.mp4"
-        cfg = VideoEncoderConfig(vcodec="h264")
+        cfg = RGBEncoderConfig(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))
@@ -578,12 +593,12 @@ class TestEncoderConfigPersistence:
 
     @require_libsvtav1
     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)
+        cfg = RGBEncoderConfig(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, rgb_encoder=cfg
         )
 
-        _add_frames(dataset, num_frames=4)
+        add_frames(dataset, num_frames=4)
         dataset.save_episode()
         dataset.finalize()
 
@@ -601,16 +616,16 @@ class TestEncoderConfigPersistence:
 
     @require_libsvtav1
     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)
+        cfg = RGBEncoderConfig(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, rgb_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()
 
@@ -618,13 +633,13 @@ class TestEncoderConfigPersistence:
 
 
 class TestFromVideoInfo:
-    """``VideoEncoderConfig.from_video_info`` reconstructs an encoder config
+    """``RGBEncoderConfig.from_video_info`` reconstructs an encoder config
     from the ``video.*`` keys persisted in a dataset's ``info.json``.
     """
 
     @require_libsvtav1
     def test_reconstructs_from_dummy_video_info(self):
-        cfg = VideoEncoderConfig.from_video_info(DUMMY_VIDEO_INFO)
+        cfg = RGBEncoderConfig.from_video_info(DUMMY_VIDEO_INFO)
 
         # Canonical stream codec ``"av1"`` is aliased to the encoder name.
         assert cfg.vcodec == "libsvtav1"
@@ -636,4 +651,220 @@ class TestFromVideoInfo:
         assert cfg.video_backend == DUMMY_VIDEO_INFO["video.video_backend"]
         # ``{}`` placeholder (typical after a merge with disagreeing sources)
         # must not leak into the reconstructed config.
-        assert cfg.extra_options == VideoEncoderConfig().extra_options
+        assert cfg.extra_options == RGBEncoderConfig().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,
+            extra_options={},
+            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,
+            extra_options={},
+            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/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.extra_options",
+                '{"x265-params": "lossless=1"}',
+                "--operation.depth_encoder.depth_max",
+                "12.0",
+                "--operation.depth_encoder.use_log",
+                "false",
+            ]
+        )
+        assert cfg.operation.depth_encoder.extra_options == {"x265-params": "lossless=1"}
+        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/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