HUGS：通过学习人类先验知识指导跨模式和尺度的统一灵巧抓取合成
HUGS: Guiding Unified Dexterous Grasp Synthesis Across Modes and Scales via Learned Human Priors
AI总结：
研究跨尺度物体的灵巧抓取问题，提出HUGS框架，通过学习物体条件下的人类先验知识指导优化，平衡成功率和多样性，提升了接触模式覆盖与合成成功率，合成大量机器人抓取数据。
摘要：
跨不同物体尺度的灵巧抓取需要从双指捏到双手抓握等多种接触模式。现有方法难以平衡合成成功率和多样性。我们提出HUGS，一个跨模式和尺度的人类先验知识指导的统一灵巧抓取合成框架。它学习物体条件下的人类先验知识，在自收集数据集上训练，合成大量机器人抓取数据，模型能在现实世界自主选择合适接触模式。
英文摘要：
Dexterous grasping across diverse object scales requires contact modes ranging from two-finger pinches to bimanual grasps. Existing dexterous grasp synthesis methods reduce the high-dimensional optimization space with manually designed expected contacts and initialization heuristics, which struggle to balance synthesis success rate and diversity. We present HUGS (Human-prior-guided Unified Dexterous Grasp Synthesis), a human-prior-guided framework for unified dexterous grasp synthesis across modes and scales. Instead of directly retargeting human demonstrations, HUGS learns an object-conditioned human prior that captures human grasp preferences and guides downstream force-closure-aware optimization. The prior is trained on a compact self-collected human grasp dataset with 1.8K grasps over 304 objects, providing broad coverage of object scales and contact modes. During synthesis, HUGS adaptively proposes contact modes and wrist initializations, substantially improving the balance between contact-mode coverage and synthesis success rate over heuristic-based methods. With HUGS, we synthesize 3.2M robotic grasps over 157K scenes, spanning object half-diagonal lengths from 2 cm to 30 cm and modes from two-finger to bimanual grasps. Models trained on the synthesized dataset autonomously select appropriate contact modes in the real world, enabling grasping from screws to large boxes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
