基于学习的腿足运动：研究现状与未来展望
Learning-based legged locomotion; state of the art and future perspectives
AI总结：
本文回顾了基于学习的腿足运动发展历程，总结了四足机器人运动技能学习方法，并展望了人形机器人双足运动方法的兴起及未来挑战。
摘要：
腿足运动承载着实现通用移动能力的前景，这对许多现实世界机器人应用而言是一项关键能力。在过去三十年中，基于模型的方法和基于学习的方法都推动了腿足运动领域的发展。然而，近年来，一系列因素显著加速了基于学习方法的进展，其中包括深度学习的兴起、机器人系统仿真技术的快速进步，以及高性能且价格可承受的硬件的普及。本文旨在简要回顾该领域的历史，总结近年来在四足机器人运动技能学习方面的工作，并为新进入该领域的研究人员提供对其中关键问题的理解。随着近期人形机器人的大量涌现，我们进一步概述了用于双足运动的类似方法的迅速崛起。最后，我们讨论了尚未解决的问题以及相关的社会影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
