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Volume 9 Issue 1
Jan.  2022

IEEE/CAA Journal of Automatica Sinica

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Yongliang Yang, Zihao Ding, Rui Wang, Hamidreza Modares and Donald C. Wunsch, "Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 47-63, Jan. 2022. doi: 10.1109/JAS.2021.1004258
Citation: Yongliang Yang, Zihao Ding, Rui Wang, Hamidreza Modares and Donald C. Wunsch, "Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 47-63, Jan. 2022. doi: 10.1109/JAS.2021.1004258

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

doi: 10.1109/JAS.2021.1004258
Funds:  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, and the Army Research Laboratory (W911NF-18-2-0260)
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  • 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.

     

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    Highlights

    • We presented a novel two-level HRI controller design framework consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design
    • In the outer-loop, the data-driven reinforcement learning technique is used for performance optimization 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

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