Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand

2026-09-15

AI Summary

研究通过强化学习方法使仿人手在保留手指设计和位置控制器的情况下，学会使用手指进行移动、支撑重量及与环境互动。

Abstract

A walking robotic hand must use the same fingers to move its body, support its weight, and interact with the environment. We show how an anthropomorphic hand can learn these skills while retaining its finger design and position controller. Onboard power and computation make the platform self-contained. Our reinforcement learning approach accounts for the hand's unequal fingers, with training in a simulator calibrated from hardware measurements. In simulation, the hand moves faster with our reward formulation than with tuned rewards originally designed for quadrupeds. On hardware, task-specific policies enable untethered crawling, steering, and fall recovery. While supporting its own weight, the hand also executes successive keyboard commands without vision and pushes an object to targets using overhead visual feedback. These results demonstrate a compact mobile manipulator that reuses its fingers for locomotion and interaction, without a separate locomotion mechanism.

Problem

• anthropomorphic hand
• locomotion
• manipulation
• reinforcement learning
• simulator

Innovation

• Anthropomorphic Hand
• Reinforcement Learning
• Self-Supported Locomotion
• Manipulation