  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
