HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments

Abstract:We present HRDexDB, a real-world 4D dexterous grasping dataset capturing 3D hand-object interaction trajectories over time across five embodiments. The dataset comprises 3.2K trials over 100 diverse objects. Using a synchronized multi-camera system and an integrated reconstruction pipeline, HRDexDB provides multi-view and egocentric RGB observations, 3D hand geometry, robot states, and object 6D pose trajectories, together with success/failure annotations. Human and robotic hands interact with shared objects, enabling the study of embodiment-dependent grasp strategies and contact patterns. We demonstrate the dataset's utility through human-to-robot contact map transfer, visual robot-object contact estimation, and retrieval-assisted grasping. Together, these results establish HRDexDB as a resource for studying and learning dexterous interactions across human and robotic embodiments.

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2604.14944 [cs.RO]

(or arXiv:2604.14944v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.14944 Focus to learn more arXiv-issued DOI via DataCite

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