Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments

TL;DR

This paper introduces a meta-learning control approach enabling robots to adapt in real-time to changing dynamics and environments, ensuring robust locomotion without pre-designed gaits.

Contribution

It presents a novel meta-reinforcement learning method that adapts control policies online for diverse and changing robot conditions, improving robustness and flexibility.

Findings

Successfully adapts to varying ground friction and external pushes

Handles robot model changes including hardware faults

Demonstrates rapid policy adaptation in simulation

Abstract

This work developed a meta-learning approach that adapts the control policy on the fly to different changing conditions for robust locomotion. The proposed method constantly updates the interaction model, samples feasible sequences of actions of estimated the state-action trajectories, and then applies the optimal actions to maximize the reward. To achieve online model adaptation, our proposed method learns different latent vectors of each training condition, which are selected online given the newly collected data. Our work designs appropriate state space and reward functions, and optimizes feasible actions in an MPC fashion which are then sampled directly in the joint space considering constraints, hence requiring no prior design of specific walking gaits. We further demonstrate the robot's capability of detecting unexpected changes during interaction and adapting control policies quickly. The extensive validation on the SpotMicro robot in a physics simulation shows adaptive and robust locomotion skills under varying ground friction, external pushes, and different robot models including hardware faults and changes.