模仿和再利用：从人类和动物行为中学习可重复使用的机器人运动技能
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
摘要：
我们研究了利用人类和动物运动的先验知识来学习真正的足式机器人的可重复使用的运动技能。我们的方法建立在以前的工作模仿人类或狗的运动捕捉（MoCap）数据，学习运动技能模块。一旦学会，这个技能模块可以重复用于复杂的下游任务。重要的是，由于MoCap数据的先验性，我们的方法不需要广泛的奖励工程来在重用时产生合理和自然的行为。这使得创建适合在真实机器人上部署的规则化、面向任务的控制器变得容易。我们演示了我们的技能模块可以用于模仿，和训练可控的步行和运球政策的ANYmal四足动物和OP3人形。然后，这些策略通过zero-shot仿真到现实的传输部署在硬件上。相关视频可在https://bit.ly/robot-npmp上获得。
英文摘要：
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitating human or dog Motion Capture (MoCap) data to learn a movement skill module. Once learned, this skill module can be reused for complex downstream tasks. Importantly, due to the prior imposed by the MoCap data, our approach does not require extensive reward engineering to produce sensible and natural looking behavior at the time of reuse. This makes it easy to create well-regularized, task-oriented controllers that are suitable for deployment on real robots. We demonstrate how our skill module can be used for imitation, and train controllable walking and ball dribbling policies for both the ANYmal quadruped and OP3 humanoid. These policies are then deployed on hardware via zero-shot simulation-to-reality transfer. Accompanying videos are available at https://bit.ly/robot-npmp.
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。
