The agility of animals, particularly in complex activities such as running, turning, jumping, and backflipping, stands as an exemplar for robotic system design. Transferring this suite of behaviors to legged robotic systems introduces essential inquiries: How can a robot be trained to learn multiple locomotion behaviors simultaneously? How can the robot execute these tasks with a smooth transition? How to integrate these skills for wide-range applications? This paper introduces the Versatile Instructable Motion prior (VIM) - a Reinforcement Learning framework designed to incorporate a range of agile locomotion tasks suitable for advanced robotic applications. Our framework enables legged robots to learn diverse agile low-level skills by imitating animal motions and manually designed motions. Our Functionality reward guides the robot's ability to adopt varied skills, and our Stylization reward ensures that robot motions align with reference motions. Our evaluations of the VIM framework span both simulation environments and real-world deployment. To the best of our knowledge, this is the first work that allows a robot to concurrently learn diverse agile locomotion skills using a single learning-based controller in the real world. Further details and supportive media can be found at our project site: https://rchalyang.github.io/VIM

翻译：动物的敏捷性，尤其是在奔跑、转向、跳跃和后空翻等复杂活动中的表现，为机器人系统设计树立了典范。将这些行为套件迁移到足式机器人系统引出了关键问题：如何训练机器人同时学习多种运动行为？如何使机器人平滑地执行这些任务？如何整合这些技能以实现广泛应用？本文提出了多用途可指令运动先验（VIM）——一种旨在整合适用于高级机器人应用的一系列敏捷运动任务的强化学习框架。我们的框架使足式机器人能够通过模仿动物运动和人工设计运动来学习多样化的敏捷低级技能。其中，功能性奖励引导机器人采纳多种技能的能力，而风格化奖励确保机器人的运动与参考运动一致。我们对VIM框架的评估涵盖了仿真环境和实际部署场景。据我们所知，这是首项使机器人能够在现实世界中利用单一基于学习的控制器同时学习多种敏捷运动技能的工作。更多详情及辅助材料请访问项目网站：https://rchalyang.github.io/VIM