通过解耦分析机械结构与驱动设计，揭示手部形态如何影响操控性能。
Morphology and actuation as inductive biases in robotic hand manipulation
- 分离分析关节运动学与驱动矩阵，用任务雅可比矩阵建模控制特性
- 斜轴设计提升拇指控制性但削弱长指表现，分叉肌腱网络改善长指控制却恶化拇指
- 适用于机器人手部设计优化与强化学习任务的结构适配性评估
机器人手在解剖精度和机械复杂度上差异显著，其结构选择影响关节运动协调与控制难度。本文提出一个统一框架，将运动学与驱动阶段分开分析，通过任务雅可比矩阵、驱动矩阵及其乘积进行建模。应用于代表两种设计哲学的双手：Shadow Dexterous Hand 和 Anatomically Correct, Biomechatronic Hand，从四个形态维度分析：关节轴几何、驱动器与自由度比、耦合架构、权威分布。所有参数均来自双手的标准数字模型。解剖精度并无普适优势：斜轴改善拇指条件性，但使长指表现劣于正交轴设计；分支肌腱网络改善所有长指的有效控制映射，但在拇指处因驱动权威集中而显著恶化。基于这些指标的预测在 PPO、DDPG+HER、TQC+HER 三种强化学习算法下，对三个任务进行了验证。
原文摘要 · Abstract (English)
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.
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