| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| - ur10e |
| - robotiq |
| - real-robot |
| - manipulation |
| configs: |
| - config_name: default |
| data_files: data/*/*.parquet |
| --- |
| |
| # ur10e-cup |
|
|
| VR-teleoperated **UR10e + Robotiq 2F** episodes of a single task: *"pick up the cup"*. |
| Recorded on a real robot cell — there is no simulator for this setup, so every |
| evaluation is a real-robot rollout. |
|
|
| This dataset was created using [LeRobot](https://github.com/huggingface/lerobot) |
| (dataset codebase **v3.0**). |
|
|
| ## Dataset summary |
|
|
| | | | |
| |---|---| |
| | robot | UR10e (6 DoF) + Robotiq 2F gripper | |
| | episodes / frames | 81 / 49,779 | |
| | fps | 20 (native, stride 1) | |
| | duration | ~41 min of teleoperation | |
| | tasks | 1 — `"pick up the cup"` | |
| | cameras | `observation.images.side`, `observation.images.wrist` — 480×640, AV1 | |
| | size | ~1.1 GB | |
| | splits | `train: 0:81` | |
|
|
| ### Features |
|
|
| | key | dtype | shape | notes | |
| |---|---|---|---| |
| | `observation.images.side` | video | (480, 640, 3) | third-person camera | |
| | `observation.images.wrist` | video | (480, 640, 3) | wrist camera | |
| | `observation.state` | float32 | (7,) | 6 UR joint angles (rad) + gripper | |
| | `action` | float32 | (7,) | **absolute** joint targets + gripper, i.e. `state[t+1]` | |
|
|
| Joint order: `shoulder_pan, shoulder_lift, elbow, wrist_1, wrist_2, wrist_3, gripper`. |
| The gripper channel is binary (`1` = open). Actions are *absolute joint targets*, not |
| deltas — this matches the SO-100/SO-101 joint-vector convention that `lerobot/smolvla_base` |
| was pretrained on. |
|
|
| ## Usage |
|
|
| ```python |
| from lerobot.datasets.lerobot_dataset import LeRobotDataset |
| |
| ds = LeRobotDataset("khanhnd61/ur10e-cup") |
| sample = ds[0] |
| print(sample["task"], sample["observation.state"], sample["action"].shape) |
| ``` |
|
|
| ## Provenance |
|
|
| Raw HDF5 recordings → frame/state extraction → **gripper-timing correction** → LeRobot v3.0 |
| conversion. The gripper fix is the reason this extract is preferred over the un-corrected one: |
| in the raw episodes the fingers start moving ~2.0 s *before* `obs_gripper` flips, so the binary |
| edge was re-timed to the measured mid-transition against the video. At 20 Hz the original lag |
| would have been ~40 steps of "gripper says open, fingers are closing". |
|
|
| ## Known caveats |
|
|
| - **`wrist_2` is a dead channel.** State/action index 4 spans `[1.5702, 1.5716]` with |
| std ≈ 0.0008 rad — the joint never moves, and that 1.4 mrad spread is just 4-decimal |
| rounding. Under MEAN_STD normalization this dimension is amplified into near-pure noise. |
| Consider dropping it. |
| - **Lossy source.** Frames come from CRF-26 H.264 and were re-encoded to AV1 |
| (~0.006 MAE round-trip). Joints are rounded to 4 decimals, gripper to 3. |
| - **Single task, single scene.** 81 episodes of one instruction — expect a policy that |
| masters this cell rather than one that generalizes. Language conditioning is effectively |
| unused. |
| - **No held-out split.** All 81 episodes are in `train`; hold some out yourself if you want |
| offline evaluation. |
|
|
| ## Training note |
|
|
| `lerobot/smolvla_base` declares its cameras as `observation.images.camera1/2/3`, so training |
| against this dataset needs a rename: |
|
|
| ``` |
| --rename_map='{"observation.images.side": "observation.images.camera1", |
| "observation.images.wrist": "observation.images.camera2"}' |
| ``` |
|
|
| The same mapping applies at rollout time. |
|
|
