Morphology and actuation as inductive biases in robotic hand manipulation

2026-09-04

AI Summary

研究通过分析机器人手的运动学和驱动阶段，解决不同设计哲学下的控制难度问题，使用条件任务雅可比矩阵等方法进行评估。

Abstract

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.

Problem

• Robotic hands
• anatomical fidelity
• mechanical complexity
• joint motion coordination
• control difficulty

Innovation

• task Jacobian
• actuation matrix
• morphological aspects
• reinforcement learning