Adaptation of Quadruped Robot Locomotion with Meta-Learning

TL;DR

This paper demonstrates that meta-reinforcement learning enables quadruped robots to adapt to various terrains and tasks efficiently, achieving performance comparable to single-task training.

Contribution

The work introduces a meta-learning approach for quadruped robot locomotion, allowing rapid adaptation across multiple tasks with performance similar to dedicated single-task training.

Findings

Meta-reinforcement learning enables versatile locomotion adaptation.

Meta-trained robot performs comparably to single-task trained robots.

Robustness across different terrains and tasks demonstrated.

Abstract

Animals have remarkable abilities to adapt locomotion to different terrains and tasks. However, robots trained by means of reinforcement learning are typically able to solve only a single task and a transferred policy is usually inferior to that trained from scratch. In this work, we demonstrate that meta-reinforcement learning can be used to successfully train a robot capable to solve a wide range of locomotion tasks. The performance of the meta-trained robot is similar to that of a robot that is trained on a single task.