学習ベースの脚式移動：最新技術と今後の展望
Learning-based legged locomotion; state of the art and future perspectives
深層学習やシミュレーション技術の発展により進歩した学習ベースの脚式移動研究を、四足歩行と二足歩行（ヒューマノイド）に分けて概観し、未解決課題と社会的影響を論じたサーベイ。
著者: Sehoon Ha, Joonho Lee, Michiel van de Panne, Zhaoming Xie, Wenhao Yu, Majid Khadiv
分類: cs.RO
原文アブストラクト
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.
