Generalized Animal Imitator: Agile Locomotion with Versatile Motion
  Prior
Ruihan Yang Zhuoqun Chen Jianhan Ma Chongyi Zheng Yiyu Chen Quan Nguyen Xiaolong Wang
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 .