Generalized Animal Imitator: Agile Locomotion with Versatile Motion Prior
This addresses the challenge of versatile and agile robotic locomotion for applications in advanced robotics, though it is incremental as it builds on prior imitation and reinforcement learning methods.
The paper tackles the problem of enabling legged robots to learn multiple agile locomotion behaviors like running and jumping simultaneously, resulting in a framework that allows a single controller to execute these tasks with smooth transitions in both simulation and real-world settings.
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 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 and the real world. Our framework allows a robot to concurrently learn diverse agile locomotion skills using a single learning-based controller in the real world. Videos can be found on our website: https://rchalyang.github.io/VIM/