Learning-based legged locomotion; state of the art and future perspectives

TL;DR

This paper reviews the evolution and recent advances in learning-based legged locomotion, highlighting key developments, current challenges, and future research directions for quadruped and bipedal robots.

Contribution

It provides a comprehensive overview of the history, recent progress, and future perspectives in learning-based legged locomotion, including societal impacts.

Findings

Rapid progress due to deep learning and simulation advancements

Growing application of learning methods in humanoid and quadruped robots

Identification of open problems and future research directions

Abstract

Legged locomotion holds the premise of universal mobility, a critical capability for many real-world robotic applications. Both model-based and learning-based approaches have advanced the field of legged locomotion in the past three decades. In recent years, however, a number of factors have dramatically accelerated progress in learning-based methods, including the rise of deep learning, rapid progress in simulating robotic systems, and the availability of high-performance and affordable hardware. This article aims to give a brief history of the field, to summarize recent efforts in learning locomotion skills for quadrupeds, and to provide researchers new to the area with an understanding of the key issues involved. With the recent proliferation of humanoid robots, we further outline the rapid rise of analogous methods for bipedal locomotion. We conclude with a discussion of open problems as well as related societal impact.