Datasets:
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observation.state list | action list | timestamp float32 0 17.3 | frame_index int64 0 518 | episode_index int64 0 191 | index int64 0 67.4k | task_index int64 0 0 | observation.human_hand.cam_head.landmarks_2d list | observation.human_hand.cam_head.landmarks_uv list | observation.human_hand.cam_head.world_landmarks list | observation.human_hand.cam_head.valid list | observation.human_hand.cam_head.score list | observation.human_hand.cam_chest.landmarks_2d list | observation.human_hand.cam_chest.landmarks_uv list | observation.human_hand.cam_chest.world_landmarks list | observation.human_hand.cam_chest.valid list | observation.human_hand.cam_chest.score list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
g1_desktop_organize_table_correction
Bimanual tabletop teleoperation on a Unitree G1 with Inspire dexterous hands, in LeRobot v2.1 format.
Task — Follow the human's corrective gesture and hand the indicated object to the human.
| Episodes | 192 |
| Frames | 67405 (37.4 min @ 30 fps) |
| Episode length | 268–519 frames (median 346) |
| State / action | 26-D / 26-D |
| Cameras | 2 × 640×480 |
State and action
Both vectors are 26-D and share the same layout:
0– 6 left arm shoulder_pitch/roll/yaw, elbow, wrist_roll/pitch/yaw
7–13 right arm shoulder_pitch/roll/yaw, elbow, wrist_roll/pitch/yaw
14–19 left hand pinky, ring, middle, index, thumb_bend, thumb_rotation
20–25 right hand pinky, ring, middle, index, thumb_bend, thumb_rotation
Hand values are real Inspire measurements in 0..1 (0 closed, 1 open), not
placeholders.
Video
| Key | Content | Codec |
|---|---|---|
observation.images.cam_chest |
cam_chest — raw camera | h264 |
observation.images.cam_chest_hand_overlay |
cam_chest_hand_overlay — hand overlay | h264 |
observation.images.cam_head |
cam_head — raw camera | h264 |
observation.images.cam_head_hand_overlay |
cam_head_hand_overlay — hand overlay | h264 |
*_hand_overlay clips are the same view with the detected human-hand skeleton
drawn on top (yellow links, orange joints).
Frame rate caveat
Timestamps run at 30 fps and observation.state changes on every frame, but the
cameras were captured slower and the recording holds the previous frame to fill in.
Measured on this dataset:
| View | Duplicated frames | Effective rate |
|---|---|---|
| — | — | — |
Consecutive video frames can therefore be pixel-identical while the state has moved. Deduplicate on the image if your model assumes every frame carries new visual information.
Human-hand landmarks
MediaPipe Hands runs on each camera view; results are stored as parquet columns, so a frame fetched from the dataset already carries them.
| Column | Shape | Contents |
|---|---|---|
observation.human_hand.<cam>.landmarks_uv |
[84] |
2 hands × 21 points × (u, v) pixels |
observation.human_hand.<cam>.landmarks_2d |
[126] |
2 × 21 × (x, y, z), normalised |
observation.human_hand.<cam>.world_landmarks |
[126] |
2 × 21 × (x, y, z), metric |
observation.human_hand.<cam>.valid |
[2] |
left / right present |
observation.human_hand.<cam>.score |
[2] |
handedness confidence |
Order is always left hand then right hand; a missing hand is zero-filled with
valid = 0, never NaN. Candidates whose landmark patches are achromatic (the
robot's own black-and-white hand) are rejected before anything is written.
uv = frame["observation.human_hand.cam_chest.landmarks_uv"].reshape(2, 21, 2)
valid = frame["observation.human_hand.cam_chest.valid"]
Loading
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("DaoyuanZhu/g1_desktop_organize_table_correction")
Published in the v2.1 layout (one parquet and one clip per episode) so it loads
on older lerobot releases directly. On v3.0+ run the official upgrade first:
python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=DaoyuanZhu/g1_desktop_organize_table_correction
License
Apache-2.0.
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