统一手部动作空间让机器人手在不同结构间无缝切换操作
Cross-Embodiment Robot Manipulation via a Unified Hand Action Space
- 用球体形变表示手部动作，跨平台通用
- 零样本迁移至未见过的手型，微调快速
- 适合多形态机器人手的灵巧操作研究
机器人操作策略通常与特定机械手结构绑定，限制了行为在不同构型平台间的迁移。本文提出统一手部动作空间（UHAS），一种基于球体的通用动作表征，将机器人手动作表示为标准球体的几何形变，并通过级联逆运动学（CIK）算法将共享表征映射到具体手型的关节配置。利用强化学习，在该动作空间中直接训练灵巧操作策略，完成物体在手中重定向任务。在多种机械手（Allegro Hand、LEAP Hand、Shadow Hand、MANO人手）的仿真与真实实验中验证，结果表明该方法实现了有效灵巧操作、未见手型的零样本迁移、快速跨平台微调及真实场景部署。实验显示，UHAS能实现稳定灵巧控制与跨手型策略迁移。
原文摘要 · Abstract (English)
Robot manipulation policies are typically tied to specific robotic hand embodiments, limiting the transfer of learned behaviors across platforms with different kinematic structures. In this work, we propose the Unified Hand Action Space (UHAS), a sphere-based unified action representation for cross-embodiment dexterous manipulation. UHAS represents robotic hand actions as geometric deformations of a canonical sphere and uses a Cascade Inverse Kinematics (CIK) algorithm to map the shared representation to embodiment-specific joint configurations. Using reinforcement learning, we train dexterous manipulation policies directly in the proposed action space for in-hand cube reorientation tasks. We evaluate our method in both simulation and real-world experiments across multiple robotic hands, including the Allegro Hand, LEAP Hand, Shadow Hand, and MANO Human Hand. Experimental results demonstrate effective dexterous manipulation, zero-shot transfer to unseen hands, rapid finetuning across embodiments, and successful real-world deployment. Our experiments show that the proposed UHAS representation enables stable dexterous control and cross-embodiment policy transfer across robotic hands.
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