构建人类与机器人双手在100种物体上的高精度抓取数据集
HRDexDB: A Paired Human-Robot Dataset for Cross-Embodiment Dexterous Grasping
- 同步采集人类与多种机器人手抓取同一物体的运动轨迹
- 包含2100次抓取实验，每条轨迹含视觉、运动、触觉信号
- 适合研究人机抓取对比与机器人灵巧操作优化
我们提出HRDexDB，一个涵盖人类与多种机器人手的高保真灵巧抓取配对数据集，覆盖100种不同物体。不同于现有数据集，该数据集提供了跨身体形态的完整抓取轨迹，包含2100次抓取实验，每条实验均配备同步的视觉与运动模态数据，并为具备触觉能力的机器人手提供接触力信号。通过先进的视觉方法与专用多相机系统，该数据集实现了对代理与被操作物体的高精度时空3D真实运动标注。其核心价值在于，在相同目标物体和相似抓取动作下，实现人类灵巧性与机器人执行的紧密对齐，为跨身体形态灵巧操作提供基准支持。
原文摘要 · Abstract (English)
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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