汎用動物模倣器：多様な運動事前分布による俊敏な移動
Generalized Animal Imitator: Agile Locomotion with Versatile Motion Prior
動物の運動を模倣しつつ手動設計の運動も取り入れた強化学習フレームワークVIMを提案し、脚式ロボットが単一の制御器で走る・曲がる・跳ぶ・バク宙など多様な俊敏移動スキルを実世界で同時に学習できるようにした。
著者: Ruihan Yang, Zhuoqun Chen, Jianhan Ma, Chongyi Zheng, Yiyu Chen, Quan Nguyen, Xiaolong Wang
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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/
