Behavior evolution-inspired approach to walking gait reinforcement training for quadruped robots

TL;DR

This paper introduces a novel reinforcement learning framework inspired by animal evolution, combining genetic algorithms and incremental learning to enhance quadruped robot gait adaptation to diverse terrains.

Contribution

It proposes a self-improvement mechanism and a new training framework that integrates genetic algorithms with reinforcement learning for better gait adaptation.

Findings

Framework improves terrain adaptability of quadruped gait

Genetic algorithm enhances initial foot trajectory selection

Simulation results show superior performance over traditional methods

Abstract

Reinforcement learning method is extremely competitive in gait generation techniques for quadrupedal robot, which is mainly due to the fact that stochastic exploration in reinforcement training is beneficial to achieve an autonomous gait. Nevertheless, although incremental reinforcement learning is employed to improve training success and movement smoothness by relying on the continuity inherent during limb movements, challenges remain in adapting gait policy to diverse terrain and external disturbance. Inspired by the association between reinforcement learning and the evolution of animal motion behavior, a self-improvement mechanism for reference gait is introduced in this paper to enable incremental learning of action and self-improvement of reference action together to imitate the evolution of animal motion behavior. Further, a new framework for reinforcement training of quadruped gait is proposed. In this framework, genetic algorithm is specifically adopted to perform global probabilistic search for the initial value of the arbitrary foot trajectory to update the reference trajectory with better fitness. Subsequently, the improved reference gait is used for incremental reinforcement learning of gait. The above process is repeatedly and alternatively executed to finally train the gait policy. The analysis considering terrain, model dimensions, and locomotion condition is presented in detail based on simulation, and the results show that the framework is significantly more adaptive to terrain compared to regular incremental reinforcement learning.