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

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arxiv:2609.17172

Published on Sep 15

· Submitted by

Amirhossein Kazemipour on Sep 17

· ETH Zurich

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Authors:

Amirhossein Kazemipour ,

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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.

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Amrkzp

Paper author Paper submitter 5 days ago

How can a robotic hand move to the work instead of being carried by an arm? We study an anthropomorphic hand that uses its fingers for self-supported locomotion and manipulation while carrying its own power and compute. We train task-specific reinforcement-learning policies in a simulator calibrated to hardware measurements, then deploy them on the hand. The system crawls untethered across indoor and outdoor surfaces, recovers from falls, presses keyboard keys without visual feedback, and pushes a cube to targets using overhead tracking.

Demo: https://youtu.be/93BLyBPAfYs Project page: https://srl-ethz.github.io/website-fingers-as-legs/

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5 days ago

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