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arXiv · 2412.09440

Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion

Joseph Humphreys·Chengxu Zhou
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Abstract

Legged robots must adapt their gait to navigate unpredictable environments, a challenge that animals master with ease. However, most deep reinforcement learning (DRL) approaches to quadruped locomotion rely on a fixed gait, limiting adaptability to changes in terrain and dynamic state. Here we show that integrating three core principles of animal locomotion-gait transition strategies, gait memory and real-time motion adjustments enables a DRL control framework to fluidly switch among multiple gaits and recover from instability, all without external sensing. Our framework is guided by biomechanics-inspired metrics that capture efficiency, stability and system limits, which are unified to inform optimal gait selection. The resulting framework achieves blind zero-shot deployment across diverse, real-world terrains and substantially significantly outperforms baseline controllers. By embedding biological principles into data-driven control, this work marks a step towards robust, efficient and versatile robotic locomotion, highlighting how animal motor intelligence can shape the next generation of adaptive machines.

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Joseph Humphreys, Chengxu Zhou. 2024-12-12. Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion. https://arxiv.org/abs/2412.09440

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