Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand
Amirhossein Kazemipour, Hehui Zheng, Robert Katzschmann
This paper teaches a robotic hand to walk, support itself, and interact with its environment using its fingers, without needing a separate locomotion system. A practitioner might care about this research because it could lead to more compact and versatile robots that can perform multiple tasks.
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.