Towards Jumping Skill Learning by Target-guided Policy Optimization for Quadruped Robots  

作　　者：Chi Zhang Wei Zou Ningbo Cheng Shuomo Zhang 

机构地区：[1]School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,100149,China [2]Institute of Automation,Chinese Academy of Sciences,Beijing,100190,China

出　　处：《Machine Intelligence Research》2024年第6期1162-1177,共16页机器智能研究（英文版）

基　　金：National Natural Science Foundation of China(No.61773374);National Key Research and Development Program of China(No.2017YFB1300104).

摘　　要：Endowing quadruped robots with the skill to forward jump is conducive to making it overcome barriers and pass through complex terrains.In this paper,a model-free control architecture with target-guided policy optimization and deep reinforcement learn-ing(DRL)for quadruped robot jumping is presented.First,the jumping phase is divided into take-off and flight-landing phases,and op-timal strategies with soft actor-critic(SAC)are constructed for the two phases respectively.Second,policy learning including expecta-tions,penalties in the overall jumping process,and extrinsic excitations is designed.Corresponding policies and constraints are all provided for successful take-off,excellent flight attitude and stable standing after landing.In order to avoid low efficiency of random ex-ploration,a curiosity module is introduced as extrinsic rewards to solve this problem.Additionally,the target-guided module encour-ages the robot explore closer and closer to desired jumping target.Simulation results indicate that the quadruped robot can realize com-pleted forward jumping locomotion with good horizontal and vertical distances,as well as excellent motion attitudes.

关 键 词：Jumping locomotion for quadruped robot policy optimization deep reinforcement learning(DRL) locomotion control robot learning 

分 类 号：O225[理学—运筹学与控制论]