---
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.