Datasets:
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observation.state list | action list | timestamp float32 0 38.7 | frame_index int64 0 1.16k | episode_index int64 0 17 | index int64 0 16.6k | task_index int64 0 0 | observation.human_hand.cam_chest.landmarks_2d listlengths 126 126 | observation.human_hand.cam_chest.landmarks_uv listlengths 84 84 | observation.human_hand.cam_chest.world_landmarks listlengths 126 126 | observation.human_hand.cam_chest.valid listlengths 2 2 | observation.human_hand.cam_chest.score listlengths 2 2 | observation.human_hand.cam_head.landmarks_2d listlengths 126 126 | observation.human_hand.cam_head.landmarks_uv listlengths 84 84 | observation.human_hand.cam_head.world_landmarks listlengths 126 126 | observation.human_hand.cam_head.valid listlengths 2 2 | observation.human_hand.cam_head.score listlengths 2 2 | observation.human_body.cam_chest.landmarks_uv listlengths 34 34 | observation.human_body.cam_chest.landmarks_2d listlengths 34 34 | observation.human_body.cam_chest.scores listlengths 17 17 | observation.human_body.cam_chest.valid float32 0 1 | observation.human_body.cam_chest.hand_anchor listlengths 6 6 | observation.human_body.cam_head.landmarks_uv listlengths 34 34 | observation.human_body.cam_head.landmarks_2d listlengths 34 34 | observation.human_body.cam_head.scores listlengths 17 17 | observation.human_body.cam_head.valid float32 0 1 | observation.human_body.cam_head.hand_anchor listlengths 6 6 | observation.eef_state listlengths 14 14 | observation.root_orientation listlengths 4 4 | observation.projected_gravity listlengths 3 3 | observation.cpp_rotation_offset listlengths 4 4 | observation.init_base_quat listlengths 4 4 | teleop.delta_heading float64 0 0.23 | action.motion_token listlengths 64 64 | teleop.smpl_joints listlengths 72 72 | teleop.smpl_pose listlengths 63 63 | teleop.body_quat_w listlengths 4 4 | teleop.target_body_orientation listlengths 6 6 | teleop.left_hand_joints listlengths 7 7 | teleop.right_hand_joints listlengths 7 7 | teleop.smpl_frame_index int64 0 5.35k | teleop.left_wrist_joints listlengths 3 3 | teleop.right_wrist_joints listlengths 3 3 | teleop.stream_mode int32 1 4 | teleop.planner_mode int32 0 0 | teleop.planner_movement listlengths 3 3 | teleop.planner_facing listlengths 3 3 | teleop.planner_speed float32 -1 -1 | teleop.planner_height float32 -1 -1 | teleop.vr_3pt_position listlengths 9 9 | teleop.vr_3pt_orientation listlengths 18 18 | observation.root_position listlengths 3 3 | sync.t_host_ns int64 1,780,397,664B 1,780,411,896B |
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End of preview. Expand in Data Studio
wb_shelf_cup
Whole-body teleoperation data from a Unitree_G1_WholeBody_RGB, published in LeRobot v2.1 format.
Published in the v2.1 layout (one parquet and one video clip per episode) so it loads directly on older
lerobotreleases. Onlerobotv3.0+ run the official upgrade first:python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=DaoyuanZhu/wb_shelf_cup
Task — pick up the cup indicated by the human's cues from the shelf, then hand it over to the human
At a glance
| Episodes | 18 |
| Frames | 16600 (9 min @ 30 fps) |
| Episode length | 708–1162 frames (median 924) |
| Cameras | 2 × 640×480 H264 |
| State / action | 43-D / 43-D joint vectors |
| Split | train 0:18 |
⚠️ Two variants — pick the right branch
The dataset ships as two branches that differ only in the dexterous-hand dimensions. Every other column (body joints, timestamps, indices) is byte-identical.
| Branch | Hand state (dims 22–28 / 36–42) | Hand action | Use it when |
|---|---|---|---|
main |
all zeros — never recorded | raw Dex3 teleop vector, includes negative values | you only need body/locomotion |
real_inspair |
Inspire estimate, 0.50–1.00 |
Inspire native command 0.50–1.00, plus a binary open/close trigger in the last slot |
you need anything hand-related |
If you train on grasping, use real_inspair. On main the hand state columns are identically zero, so a model will silently learn nothing from them.
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("DaoyuanZhu/wb_shelf_cup", revision="real_inspair")
Schema
observation.state and action are both 43-D, ordered as:
0–11 legs left/right hip_pitch, hip_roll, hip_yaw, knee, ankle_pitch, ankle_roll
12–14 waist yaw, roll, pitch
15–21 left arm shoulder_pitch/roll/yaw, elbow, wrist_roll/pitch/yaw
22–28 left hand index_0, index_1, middle_0, middle_1, thumb_0, thumb_1, thumb_2
29–35 right arm shoulder_pitch/roll/yaw, elbow, wrist_roll/pitch/yaw
36–42 right hand index_0, index_1, middle_0, middle_1, thumb_0, thumb_1, thumb_2
In real_inspair, the Inspire hand reports a 6-D estimate: slots 22–27 / 36–41 carry values and slot 28 / 42 stays zero-padded. On the action side the same slots carry the native command and the last slot is a binary trigger (0.0 / 1.0).
The hand state on
real_inspairis estimated, transferred from captures that did record Inspire feedback — the legacy rig had no hand sensing. Describe it as estimated/transferred Inspire hand state, not as recorded ground truth.
Teleoperation stream
Beyond the 43-D joint state and action, every frame carries the raw teleoperation and controller signals the capture recorded:
| Column | Shape | Content |
|---|---|---|
action.motion_token |
(64,) | SONIC motion token |
observation.eef_state |
(14,) | end-effector: left/right wrist position and quaternion |
observation.projected_gravity |
(3,) | gravity in the base frame |
observation.root_position |
(3,) | base position |
observation.root_orientation |
(4,) | base orientation quaternion |
observation.init_base_quat |
(4,) | base quaternion at episode start |
observation.cpp_rotation_offset |
(4,) | controller rotation offset |
teleop.left_hand_joints |
(7,) | raw left-hand teleop joints (Dex3 order) |
teleop.right_hand_joints |
(7,) | raw right-hand teleop joints |
teleop.left_wrist_joints |
(3,) | left wrist roll/pitch/yaw |
teleop.right_wrist_joints |
(3,) | right wrist roll/pitch/yaw |
teleop.smpl_joints |
(72,) | SMPL joint positions of the operator |
teleop.smpl_pose |
(63,) | SMPL pose parameters |
teleop.body_quat_w |
(4,) | operator body quaternion |
teleop.target_body_orientation |
(6,) | target body orientation (6-D) |
teleop.vr_3pt_position |
(9,) | VR three-point tracker positions |
teleop.vr_3pt_orientation |
(18,) | VR three-point tracker orientations |
teleop.planner_mode |
(1,) | locomotion mode |
teleop.planner_movement |
(3,) | commanded movement vector |
teleop.planner_facing |
(3,) | commanded facing vector |
teleop.planner_speed |
(1,) | commanded speed |
teleop.planner_height |
(1,) | commanded base height |
teleop.delta_heading |
(1,) | heading delta |
teleop.stream_mode |
(1,) | teleop stream mode |
teleop.smpl_frame_index |
(1,) | index into the SMPL stream |
sync.t_host_ns |
(1,) | host timestamp in nanoseconds |
Video
| Key | Content | Codec |
|---|---|---|
observation.images.cam_chest |
cam_chest — raw camera | h264 |
observation.images.cam_chest_hand_overlay |
cam_chest — MediaPipe hand landmarks | h264 |
observation.images.cam_chest_pose_overlay |
cam_chest — RTMO body skeleton | h264 |
observation.images.cam_head |
cam_head — raw camera | h264 |
observation.images.cam_head_hand_overlay |
cam_head — MediaPipe hand landmarks | h264 |
observation.images.cam_head_pose_overlay |
cam_head — RTMO body skeleton | h264 |
All video is H.264 and plays in any standard player.
Pose annotations
Keypoints ride along as parquet columns, one row per frame — no separate annotation files.
| Column | Source | Content |
|---|---|---|
observation.human_hand.<cam>.landmarks_uv |
MediaPipe Hands | 21 points per hand, pixel u/v, left slot then right |
observation.human_hand.<cam>.landmarks_2d |
MediaPipe Hands | the same points, normalised x/y/z |
observation.human_hand.<cam>.world_landmarks |
MediaPipe Hands | metric-like world x/y/z |
observation.human_hand.<cam>.valid / .score |
— | left/right presence flag and handedness confidence |
observation.human_body.<cam>.landmarks_uv |
RTMO rtmo-m_16xb16-600e_body7 |
COCO-17 body, pixel u/v |
observation.human_body.<cam>.landmarks_2d |
RTMO | the same points, normalised |
observation.human_body.<cam>.scores / .valid |
— | per-keypoint confidence and a per-frame flag |
Zero-filled coordinates with valid = 0 mean nothing was detected; there are no NaNs.
How the annotations were filtered
Body detections are post-processed per episode: score threshold, duplicate suppression by bounding-box containment (plain IoU misses a sprawling false box that swallows the real one), fragment merging, then a single-identity lock built from motion tracklets merged on a saturation-masked torso-hue histogram. Tracklets that overlap in time are never merged, since co-visible detections cannot be the same person.
Hand candidates are additionally anchored to the central person's wrist — a detection further than half a shoulder-width from that wrist is rejected, which is what keeps hand-shaped props out of the annotations.
Layout
data/chunk-000/episode_000000.parquet one parquet per episode
videos/chunk-000/<video_key>/episode_000000.mp4 one clip per episode
meta/info.json feature schema
meta/episodes.jsonl episode index and lengths
meta/tasks.jsonl task strings
meta/stats.json per-feature statistics
Loading
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("DaoyuanZhu/wb_shelf_cup", revision="real_inspair")
print(ds.meta.features.keys())
sample = ds[0]
License
Apache-2.0.
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