Add dataset card

README.md

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+---
+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.