形态与驱动作为机器人手操作中的归纳偏置

中文摘要 AI 辅助

机器人手在解剖学保真度和机械复杂度上差异很大，这些结构选择会影响关节运动的协调以及系统的控制难度。本文提出一个统一框架，通过对任务雅可比、驱动矩阵及其乘积的条件分析，分别分析运动学和驱动阶段，并研究二者的组合作用。该框架被应用于代表对立设计理念的两款机器人手：Shadow灵巧手和解剖学正确的生物机电手，涉及四个形态方面：关节轴几何、驱动器与自由度（DOF）的比率、耦合架构以及权限分布。所有参数均来自两款手的标准数字表示。解剖学保真度并无统一优势：倾斜轴可改善拇指的条件性，但使长手指的条件性比正交轴设计更差；分支肌腱网络可提升每根长手指的有效控制映射，却会显著恶化拇指的有效控制映射，因为拇指的驱动器权限集中于拇指对掌。基于这些指标的预测，通过使用PPO、DDPG+HER和TQC+HER三种强化学习算法，在三项不同任务上进行了验证。

英文摘要

Robotic hands vary widely in anatomical fidelity and mechanical complexity, and these structural choices influence the coordination of joint motions and the difficulty of controlling the system. A unified framework is presented in which the kinematic and actuation stages are analysed separately and in composition, through the conditioning of the task Jacobian, the actuation matrix, and their product. It is applied to two hands representing opposing design philosophies, the Shadow Dexterous Hand and the Anatomically Correct, Biomechatronic Hand, along four morphological aspects: joint axis geometry, actuator-to-DOF ratio, coupling architecture, and authority distribution. All parameters are derived from the hands' canonical digital representations. Anatomical fidelity carries no uniform advantage: oblique axes improve thumb conditioning but leave the long fingers worse conditioned than the orthogonal-axis design, while the branching tendon network improves the effective control mapping at every long finger and worsens it significantly at the thumb, where actuator authority is concentrated on thumb opposition. Predictions derived from these metrics are evaluated against reinforcement learning experiments using PPO, DDPG+HER, and TQC+HER, across three different tasks.

发表机构

• Pázmány Péter Catholic University（帕兹曼尼·彼得天主教大学）

机构由 AI 辅助整理，请以论文原文为准。