Datasets:
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 (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
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_2is 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.