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

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

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

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