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 robot. 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 design. The 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 inner-loop. Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters. In the inner-loop, a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement. On this basis, an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space. The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education,School of Automation&Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;School of Automation,Beijng Institute Technology,Beijing 100081,China;State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;Mechanical Engineering Department,Michigan State University,East Lansing,MI 48824 USA;Department of Electrical&Computer Engineering,Missouri University of Science&Technology,Rolla,MO 65401 USA

[1]Yongliang Yang-.Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning)[J].自动化学报（英文版）,2022(01):47-63

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