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arXiv · 2604.14944

HRDexDB: A Paired Human-Robot Dataset for Cross-Embodiment Dexterous Grasping

Jongbin Lim·Taeyun Ha·Mingi Choi·Jisoo Kim·Byungjun Kim·Subin Jeon·Hanbyul Joo
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Abstract

We present HRDexDB, a paired cross-embodiment dexterous grasping dataset of high-fidelity dexterous grasping sequences featuring both human and diverse robotic hands. Unlike existing datasets, HRDexDB provides a comprehensive collection of grasping trajectories across human hands and multiple robot hand embodiments, spanning 100 diverse objects. Leveraging state-of-the-art vision methods and a dedicated multi-camera system, HRDexDB offers high-precision spatiotemporal 3D ground-truth motion for both the agent and the manipulated object. The dataset comprises 2.1K grasping trials, each enriched with synchronized visual and kinematic modalities, with contact-force signals available for tactile-enabled robotic hands. By providing closely aligned captures of human dexterity and robotic execution on the same target objects under comparable grasping motions, HRDexDB serves as a foundational benchmark for cross-embodiment dexterous manipulation.

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Jongbin Lim, Taeyun Ha, Mingi Choi, Jisoo Kim, Byungjun Kim, Subin Jeon, Hanbyul Joo. 2026-04-16. HRDexDB: A Paired Human-Robot Dataset for Cross-Embodiment Dexterous Grasping. https://arxiv.org/abs/2604.14944

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