Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. While manually-designed controllers have been able to emulate many complex behaviors, building such controllers involves a time-consuming and difficult development process, often requiring substantial expertise of the nuances of each skill. Reinforcement learning provides an appealing alternative for automating the manual effort involved in the development of controllers. However, designing learning objectives that elicit the desired behaviors from an agent can also require a great deal of skill-specific expertise. In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals. We show that by leveraging reference motion data, a single learning-based approach is able to automatically synthesize controllers for a diverse repertoire behaviors for legged robots. By incorporating sample efficient domain adaptation techniques into the training process, our system is able to learn adaptive policies in simulation that can then be quickly adapted for real-world deployment. To demonstrate the effectiveness of our system, we train an 18-DoF quadruped robot to perform a variety of agile behaviors ranging from different locomotion gaits to dynamic hops and turns.

中文翻译：

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通过模仿动物学习敏捷机器人运动技能

再现动物多样化和敏捷的运动技能一直是机器人技术中的一个长期挑战。虽然手动设计的控制器已经能够模拟许多复杂的行为，但构建这样的控制器涉及一个耗时且困难的开发过程，通常需要对每项技能的细微差别有丰富的专业知识。强化学习为自动化控制器开发中涉及的手动工作提供了一种有吸引力的替代方案。然而，设计从代理中引出所需行为的学习目标也可能需要大量特定于技能的专业知识。在这项工作中，我们提出了一个模仿学习系统，使有腿机器人能够通过模仿现实世界的动物来学习敏捷的运动技能。我们表明，通过利用参考运动数据，一种单一的基于学习的方法能够自动合成控制器，用于腿式机器人的各种行为。通过将样本高效的域适应技术纳入训练过程，我们的系统能够在模拟中学习自适应策略，然后可以快速适应现实世界的部署。为了证明我们系统的有效性，我们训练了一个 18-DoF 四足机器人来执行从不同的运动步态到动态跳跃和转弯的各种敏捷行为。