Datasets:

DaoyuanZhu
/

g1_desktop_pick_pineapple_bun_from_toaster

observation.state list	action list	timestamp float32 0 38.2	frame_index int64 0 1.15k	index int64 0 9.1k	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_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
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g1_desktop_pick_pineapple_bun_from_toaster

Bimanual tabletop teleoperation on a Unitree G1 with Inspire dexterous hands, in LeRobot v2.1 format.

Task — Pick the pineapple bun from the toaster and place it on the plate.


Episodes	11
Frames	9098 (5.1 min @ 30 fps)
Episode length	655–1147 frames (median 768)
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
`cam_head`	10.5%	~26.9 fps
`cam_chest`	39.4%	~18.2 fps

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_pick_pineapple_bun_from_toaster")

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_pick_pineapple_bun_from_toaster

License

Apache-2.0.

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