Datasets:
wb_push_cart_stop_go
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_push_cart_stop_go
Task — push the cart, stop when the human raises a hand, and continue when the human waves again
At a glance
| Episodes | 61 |
| Frames | 35959 (20 min @ 30 fps) |
| Episode length | 473–737 frames (median 586) |
| Cameras | 2 × 640×480 H264 |
| State / action | 41-D / 43-D joint vectors |
| Split | train 0:61 |
Hand state layout
This main revision stores a compact 41-D observation.state: the two unused padding slots from the legacy 43-D layout are removed. The 12 hand values come from the matching real_inspair state, six per hand. action remains the original 43-D command vector.
The matching real_inspair revision retains the original 43-D layout, with six Inspire state values plus one zero-padded slot per hand.
Schema
observation.state is 41-D and action remains 43-D:
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
22–27 left hand pinky, ring, middle, index, thumb_pitch, thumb_yaw
28–34 right arm shoulder_pitch/roll/yaw, elbow, wrist_roll/pitch/yaw
35–40 right hand pinky, ring, middle, index, thumb_pitch, thumb_yaw
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 41-D state and 43-D 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_push_cart_stop_go", revision="real_inspair")
print(ds.meta.features.keys())
sample = ds[0]
License
Apache-2.0.
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