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:
this https URL .

这篇论文介绍了一种名为 Versatile Instructable Motion (VIM) 的增强学习框架，该框架旨在将多种灵活的运动任务应用于先进的机器人应用中。这个框架使得四肢机器人能够通过模仿动物动作和人工设计的动作来学习各种灵活的低级技能，通过功能奖励和风格化奖励实现任务的平滑过渡和性能对齐。这是第一个允许机器人使用单一控制器同时学习多种灵活运动任务的工作。