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The agility of animals, particularly in complex activities such as running,turning, jumping, and backflipping, stands as an exemplar for robotic systemdesign. Transferring this suite of behaviors to legged robotic systemsintroduces essential inquiries: How can a robot be trained to learn multiplelocomotion behaviors simultaneously? How can the robot execute these tasks witha smooth transition? And what strategies allow for the integrated applicationof these skills? This paper introduces the Versatile Instructable Motion prior(VIM) - a Reinforcement Learning framework designed to incorporate a range ofagile locomotion tasks suitable for advanced robotic applications. Ourframework enables legged robots to learn diverse agile low-level skills byimitating animal motions and manually designed motions with Functionalityreward and Stylization reward. While the Functionality reward guides therobot's ability to adopt varied skills, the Stylization reward ensuresperformance alignment with reference motions. Our evaluations of the VIMframework span both simulation environments and real-world deployment. To ourunderstanding, this is the first work that allows a robot to concurrently learndiverse agile locomotion tasks using a singular controller. Further details andsupportive media can be found at our project site:https://rchalyang.github.io/VIM .

Generalized Animal Imitator: Agile Locomotion with Versatile Motion Prior