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? And what strategies allow for the integrated application of these skills? 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 with Functionality reward and Stylization reward. While the Functionality reward guides the robot's ability to adopt varied skills, the Stylization reward ensures performance alignment with reference motions. Our evaluations of the VIM framework span both simulation environments and real-world deployment. To our understanding, this is the first work that allows a robot to concurrently learn diverse agile locomotion tasks using a singular controller. 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所展现的能力，为机器人系统设计树立了典范。将这一系列行为迁移到足式机器人引出了关键性问题：如何训练机器人同时学习多种运动行为？如何使机器人以平滑过渡的方式执行这些任务？以及采用何种策略实现这些技能的综合应用？本文提出了多功能可指示运动先验（VIM）——一种为整合适用于高级机器人应用的一系列敏捷运动任务而设计的强化学习框架。我们的框架通过功能奖励和风格化奖励，使足式机器人能够模仿动物运动及人工设计运动，从而学习多样化的敏捷低级技能。功能奖励指导机器人掌握不同技能的能力，而风格化奖励则确保其表现与参考运动一致。我们对VIM框架的评估涵盖了仿真环境与真实场景部署。据我们所知，这是首个允许机器人通过单一控制器并行学习多种敏捷运动任务的工作。更多细节及支持材料可见于我们的项目网站：https://rchalyang.github.io/VIM。