chrockey/DexJoCo-preprocessed

DexJoCo, preprocessed

Training frames for DexJoCo (Franka + Allegro, MuJoCo, 11 tasks), re-rendered from the official demos: RGB-D at 224 x 224 with body ids, camera poses, robot state and actions. MIT, like the DexJoCo data it is made from (License).

Dataset versions

Dataset versions are named data-vN (tags of this repo; tags never move). Always download a tag and check it with its manifest.

dataset	revision	date	demos	GB	change
data-v1	110a68c5	10-06	1,059	113.5	first upload (incl. 42 recovered unlock_ipad demos)
data-v2	421daed6	10-28	1,100	119.9	+ 41 recovered demos
data-v3	72e98046	10-28	1,100	119.7	5 frozen-start demos trimmed
data-v4	tag	11-01	1,100	22.3	data-v3 in LeRobot v3 (no H5): RGB H.264 crf 18 (lossy, 31.4 dB worst frame), depth / body id lossless; not yet validated for training

Manifest sha256 (find . -name '*.h5' -print0 | LC_ALL=C sort -z | xargs -0 sha256sum | sha256sum):

dataset	sha256
data-v1	1dca8061623db35321e1e3f8df73991ed314e398fca9d771f71204cda24ce5a3
data-v2	91c2468b9bd8a411a918914794736c4d294529e3d6d4ea89c92f13422985f7b7
data-v3	6ff159cb75a86051a46a705c13a4d9e22f26a093cdeefbf269c359d524cd9db9
data-v4	3eeda88c370159604998c3ab85dca0ad84bcf191b1a57d528cdea4ca5fc4be0b (every file in the task folders)

Download and check

hf download chrockey/DexJoCo-preprocessed --repo-type dataset --revision data-v3 --local-dir DexJoCo-preprocessed
hf download chrockey/DexJoCo-preprocessed versions/data-v3.sha256 --repo-type dataset --local-dir manifests
cd DexJoCo-preprocessed && sha256sum -c --quiet ../manifests/versions/data-v3.sha256

--revision data-v4 gives LeRobot v3 (one dataset per task: <task>/{meta,data,videos,sidecars}; lerobot>=0.6.1 reads RGB + state; depth and body id are lossless FFV1 and need a custom reader); data-v1..data-v3 give H5. Add --include "water_plant/*" for one task. Always pin --revision.

Contents

task	arms	demos	frames	cameras	GB
bimanual_assembly	2	100	72,642	back, handcam_rgb_left/right	16.5
bimanual_hanoi	2	100	109,105	back, handcam_rgb_left/right	26.3
bimanual_microwave_cook	2	100	84,790	back, handcam_rgb_left/right	20.6
bimanual_photograph	2	100	40,255	back, handcam_rgb_left/right	8.3
bimanual_unlock_ipad	2	100	53,144	back, handcam_rgb_left/right	12.1
click_mouse	1	100	39,982	front, handcam_rgb	5.4
fold_glasses	1	100	57,900	front, handcam_rgb	8.8
hammer_nail	1	100	25,034	front, handcam_rgb	4.3
pick_bucket	1	100	46,910	front, handcam_rgb	7.3
pinch_tongs	1	100	43,441	front, handcam_rgb	5.9
water_plant	1	100	27,753	front, handcam_rgb	4.3

Layout <task>/<task>_demo_<k>_<date>_<time>.h5 (unlock_ipad files: ipad_unlock_demo_*). Counts are for data-v3.

H5 schema (T frames at 30 Hz)

key	shape	meaning
attrs		task, prompt, n_arm, n_frames, size, success, cameras; trim_start in trimmed demos
actions	(≤T, 23·n_arm)	per arm [pos3, quat4, hand16], arms interleaved; may stop before T
proprio	(T, 23·n_arm)	[tcp pose 7·n_arm | hand joints 16·n_arm] (blocked)
qpos	(T, nq)	MuJoCo qpos
body_names, is_robot, is_hand	(B,)	bodies and robot / hand masks
xpos, xquat	(T, B, 3/4)	body poses
<cam>/rgb, /depth, /body_id	(T, 224, 224, ·)	RGB, depth (m, float16), body id
<cam>/intrinsic, /extrinsic	(3, 3), (T, 4, 4)	camera intrinsics, per-frame pose

How it was made

• Simulator brave-eai/dexjoco 8d23b0f; raw demos from DexJoCo/DexJoCo-Datasets-Raw.
• Each demo is replayed in the simulator, holding the last action 60 steps (hanoi 90, unlock_ipad 150, photograph 0), and rendered at 224 x 224.
• Every demo succeeds on replay. 83 needed recovery: 42 unlock_ipad (wrong iPad pose, brave-eai/dexjoco#27) and 41 marginal-contact demos (initial object pose search ≤ 3.2 mm).
• data-v3 trims 5 demos whose recording is frozen at the start (click_mouse 2, microwave_cook 3).
• Checked against the raw recordings.

License

MIT (LICENSE). Made from DexJoCo/DexJoCo-Datasets-Raw (MIT, Copyright (c) 2026 Dexjoco contributors) with the DexJoCo simulator (MIT). The rendered robots are the robot models bundled with DexJoCo: Franka Emika Panda (Apache-2.0) and Wonik Allegro hand (BSD-2-Clause, Copyright (c) 2016, SimLab); no model files are included here. If you use this data, please cite DexJoCo:

@misc{wang2026dexjocobenchmarktoolkittaskoriented,
      title={DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo},
      author={Hanwen Wang and Weizhi Zhao and Xiangyu Wang and Siyuan Huang and He Lin and Boyuan Zheng and Rongtao Xu and Gang Wang and Yao Mu and He Wang and Lue Fan and Hongsheng Li and Zhaoxiang Zhang and Tieniu Tan},
      year={2026},
      eprint={2605.16257},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}