| --- |
| license: cc-by-4.0 |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| - robotics |
| - manipulation |
| - yam |
| - depth |
| - bimanual |
| configs: |
| - config_name: default |
| data_files: data/*/*.parquet |
| --- |
| |
| # kitchen_organization |
| |
| Multi-task kitchen tidying (3 tasks): placing plates, cups and spoons onto a dish rack, including a hand-to-hand pass. |
| |
| Real-robot bimanual manipulation data collected on a **YAM** arm pair, released as part |
| of the Flex-π project. Stored in **LeRobot v2.1** format with **synchronized RGB and |
| metric depth** from three cameras. |
| |
| ## At a glance |
| |
| | | | |
| |---|---| |
| | Episodes | 248 | |
| | Frames | 146,025 | |
| | Duration | ~1.4 h @ 30 fps | |
| | Tasks | 3 | |
| | Robot | `yam` (bimanual) | |
| | Cameras | `cam_high`, `cam_left_wrist`, `cam_right_wrist` | |
| | RGB | 640×360, H.264 `.mp4` | |
| | Depth | 640×360, FFV1 `.mkv`, uint16 **millimetres** | |
| | State / action | 32-D / 32-D | |
| | LeRobot version | `v2.1` | |
|
|
| ## Tasks |
|
|
| 0. Pick up the plate on the table, pass the plate to anther hand and put them onto the rack. |
| 1. Pick up the cup on the table and put them onto the rack. |
| 2. Pick up the spoon on the table one by one and put them onto the rack |
|
|
| ## Layout |
|
|
| ``` |
| meta/ |
| info.json # feature schema, totals, chunking |
| tasks.jsonl # task_index -> natural-language instruction |
| episodes.jsonl # per-episode length + task |
| episodes_stats.jsonl # per-episode min/max/mean/std for state & action |
| camera_intrinsics.json # per-camera pinhole K at stored resolution |
| data/chunk-{NNN}/episode_{NNNNNN}.parquet |
| videos/chunk-{NNN}/observation.images.{cam}/episode_{NNNNNN}.mp4 # RGB |
| videos/chunk-{NNN}/observation.depth_ffv1.{cam}/episode_{NNNNNN}.mkv # depth |
| ``` |
|
|
| Episodes are indexed `0 .. 247`, chunked at |
| 1000 episodes (1 chunk). |
| The parquet `index` column is a **global** frame counter running |
| `0 .. 146,024` across the whole dataset. |
|
|
| ## Camera intrinsics |
|
|
| Pinhole `K` at the stored 640×360 resolution, averaged over episodes: |
|
|
| | camera | fx | fy | cx | cy | |
| |---|---|---|---|---| |
| | `cam_high` | 262.27 | 262.11 | 320.51 | 183.18 | |
| | `cam_left_wrist` | 365.72 | 365.50 | 318.08 | 182.79 | |
| | `cam_right_wrist` | 366.90 | 366.67 | 324.67 | 171.96 | |
|
|
| ## Reading the depth |
|
|
| > **The depth streams are an extension to stock LeRobot.** They are declared with |
| > `dtype: "depth_video"` (not `"video"`) in `meta/info.json` precisely so that the |
| > stock `LeRobotDataset` loader skips them — you get a working RGB dataset out of the |
| > box, and depth needs the decoder below. |
| |
| Each depth frame is a single-channel **uint16, millimetre** map, FFV1-encoded in |
| `gray16le` inside a Matroska container. `0` means no return. To decode a frame: |
| |
| ```python |
| import av, numpy as np |
| |
| with av.open("videos/chunk-000/observation.depth_ffv1.cam_high/episode_000000.mkv") as c: |
| for frame in c.decode(video=0): |
| depth_mm = frame.to_ndarray(format="gray16le").astype(np.uint16) # (H, W) |
| depth_m = depth_mm.astype(np.float32) / 1000.0 |
| ``` |
| |
| FFV1 is lossless, so the decoded values are bit-exact with what the sensor reported. |
| Do **not** transcode these to a lossy codec. |
|
|
| ## State and action layout |
|
|
| `observation.state` and `action` are both 32-D. The vector is grouped **by field**, not |
| by arm: |
|
|
| | index | contents | |
| |---|---| |
| | `0:3` | `left_pos_{x,y,z}` — left end-effector position | |
| | `3:9` | `left_rot6d_{0..5}` — left end-effector rotation, 6-D representation | |
| | `9:12` | `right_pos_{x,y,z}` | |
| | `12:18` | `right_rot6d_{0..5}` | |
| | `18:20` | `left_gripper`, `right_gripper` | |
| | `20:26` | `left_joint_{0..5}` | |
| | `26:32` | `right_joint_{0..5}` | |
|
|
| The authoritative per-dimension names are in `meta/info.json` under |
| `features.observation.state.names`. The 6-D rotation is the **first two rows** of the |
| 3×3 rotation matrix, row-major flattened (Zhou et al., *On the Continuity of Rotation |
| Representations*); recover `R` by Gram–Schmidt on those two rows and their cross |
| product. |
|
|
| ## Loading |
|
|
| RGB only, with stock LeRobot: |
|
|
| ```python |
| from lerobot.common.datasets.lerobot_dataset import LeRobotDataset |
| ds = LeRobotDataset("flex-pi/kitchen_organization") |
| ``` |
|
|
| RGB + depth: use the depth-aware loader from the Flex-π codebase. |
|
|
| ## Provenance |
|
|
| Built from source recordings |
| `uniflow_pass_and_put_plate_on_the_rack`, `uniflow_put_cup_on_the_rack`, `uniflow_put_plate_on_the_rack`, `uniflow_put_spoon_on_rack`. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the Flex-π project. |
|
|
