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siyuhsu/StellaVLA-Data-RoboDojo

StellaVLA — RoboDojo trajectories robodojo_lerobot.tar 120 GB unpack in place -> robodojo_lerobot_v30/ Byte-identical copy of the joint variant of the RoboDojo LeRobot v3.0 export, kept next to the StellaVLA results so the trajectories and their annotations are guaranteed to line up. Contents of the tar: path size notes robodojo_lerobot_v30/meta/ 54 MB includes the modality.json upstream does not ship robodojo_lerobot_v30/data/ 262 MB 14-D joint state + action… See the full description on the dataset page: https://huggingface.co/datasets/siyuhsu/StellaVLA-Data-RoboDojo.

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StellaVLA — RoboDojo trajectories

robodojolerobot.tar 120 GB unpack in place -> robodojolerobot_v30/

Byte-identical copy of the joint variant of the RoboDojo LeRobot v3.0 export, kept next to the StellaVLA results so the trajectories and their annotations are guaranteed to line up. Contents of the tar:

pathsizenotes
robodojo_lerobot_v30/meta/54 MBincludes the modality.json upstream does not ship
robodojo_lerobot_v30/data/262 MB14-D joint state + action parquet
robodojo_lerobot_v30/videos/observation.images.cam_high/48 GBhead camera — what the CoT is grounded in
robodojo_lerobot_v30/videos/observation.images.cam_left_wrist/32 GB
robodojo_lerobot_v30/videos/observation.images.cam_right_wrist/33 GB

3500 episodes / 35 tasks / 1,859,602 frames, 25 fps, ARX X5 bimanual, 480x640. State and action are 14-D: [left_arm(6), left_gripper, right_arm(6), right_gripper].

Annotations (per-episode grounding chain-of-thought for exactly these episodes) are robodojo/robodojo_cot.tar in siyuhsu/StellaVLA-Data.

Upstream: `RoboDojo-Benchmark/RoboDojo` data/RoboDojo_lerobot_v30_video — unmodified. Benchmark: https://robodojo-benchmark.com (arXiv:2607.04434).

Do not substitute RoboDojo_ee_lerobot_v30_video. That variant is 16-D end-effector state and its episodes are split differently (ep1000 is 363 frames there vs 513 here), so its videos do not line up with the annotations frame for frame.

Code: https://github.com/siyuhsu/StellaVLA