  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.
