A Novel Framework for Adaptive Quadruped Robot Locomotion Learning in Uncertain Environments

Learning diverse and flexible locomotion strategies in uncertain environments has been a longstanding challenge for quadruped robots. Although recent progress in domain randomization has partially tackled this difficulty by training policies on a wide range of potential factors, there is still a great need for improving efficiency. In this paper, we propose a novel framework for adaptive quadruped robot locomotion learning in uncertain environments. Our method is based on data-efficient reinforcement learning and learns simulation parameters iteratively. We also propose a novel Sampling-Interval-Adaptive Identification (SIAI) strategy that uses historical parameters to optimize sampling distribution and then improve identification accuracy. Final evaluations based on multiple robotic locomotion tasks showed superiority of our method over baselines.