HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 45 of its 53 references that are in the map. First posted on arXiv 16 Apr 2026.
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45 of 53 references.
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33 more
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Citing papers per year: 2026: 2.
2 papers.