UniCross：统一跨技能灵巧操作合成

中文摘要 AI 辅助

许多灵巧操作任务要求物体在交互过程中始终被牢固握持。从手-物相对运动的角度来看，这类操作包含四个典型技能：抓取、重定位、手中旋转和手中平移。人类的手可以灵活组合这些技能来完成复杂任务。然而，现有方法通常用特定技能的动作约束、目标甚至专用手型来分别建模这些技能，这破坏了长时程组合所需的兼容性和连续性。本研究提出了一个统一框架，将所有四个技能建模为单一公式，共享相同的状态和动作空间以及共同的目标结构。该公式可直接提炼出单一跨技能策略，该策略在每项技能上均表现出色，能泛化到未见过的物体，对干扰具有鲁棒性，并能将技能无缝链接为长时程操作。该框架还可在不同手型之间有效迁移。总体而言，研究结果表明，不同的灵巧操作技能可被视为共享任务公式的实例，揭示了不同行为之间的内在一致性。

英文摘要

Many dexterous manipulation tasks require the object to remain securely held throughout the interaction. From the perspective of hand-object relational motion, such manipulation comprises four canonical skills: grasping, relocation, in-hand rotation, and in-hand translation. Human hands flexibly compose these skills to accomplish complex tasks. Existing approaches, however, model these skills separately with skill-specific action constraints, objectives, or even dedicated hand morphologies, which breaks the compatibility and continuity required for long-horizon composition. In this work, we present a unified framework that models all four skills in a single formulation that shares the same state and action spaces and a common objective structure. This formulation enables distillation of a single cross-skill policy conditioned on the relational motion objectives, which achieves strong performance across all four skills, generalizes to unseen objects, remains robust to disturbances, and chains skills into long-horizon manipulation without switching policies. The framework also transfers effectively across different hand morphologies. Overall, our results suggest that different dexterous manipulation skills can be viewed as instantiations of a shared task formulation, revealing the intrinsic consistency. Project page: https://zdchan.github.io/UniCross/

发表机构

• ETH Zürich（苏黎世联邦理工学院）
• inspire AG（inspire AG公司）
• HKUST (Guangzhou)（香港科技大学（广州））

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Project page: https://zdchan.github.io/UniCross/