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

TL;DR

This paper introduces a bio-inspired DRL framework that enables quadruped robots to adaptively switch gaits and recover stability in diverse terrains without external sensing, mimicking animal locomotion strategies.

Contribution

It integrates biomechanics-inspired metrics and principles of animal gait transition, enabling versatile, zero-shot gait adaptation and stability recovery in quadruped robots.

Findings

Achieves fluid gait switching and stability recovery without external sensing.

Outperforms baseline controllers across diverse terrains.

Demonstrates robustness and versatility in real-world environments.

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.