Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

作     者：Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Yongliang Yang;Zihao Ding;Rui Wang;Hamidreza Modares;Donald C.Wunsch

作者机构：Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationSchool of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of AutomationBeijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering DepartmentMichigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer EngineeringMissouri University of Science&TechnologyRollaMO 65401 USA 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2022年第9卷第1期

页      面：47-63页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 

基　　金：This work was supported in part by the National Natural Science Foundation of China(61903028) the Youth Innovation Promotion Association,Chinese Academy of Sciences(2020137) the Lifelong Learning Machines Program from DARPA/Microsystems Technology Office the Army Research Laboratory(W911NF-18-2-0260) 

主　　题：Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free 

摘      要：In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.