Data-Driven Human-Robot Interaction Without Velocity Measurement using Off-Policy Reinforcement Learning
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.
Y. Yang et al., "Data-Driven Human-Robot Interaction Without Velocity Measurement using Off-Policy Reinforcement Learning," IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 1, pp. 47 - 63, Institute of Electrical and Electronics Engineers (IEEE), Jan 2022.
The definitive version is available at https://doi.org/10.1109/JAS.2021.1004258
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Impedance Control; Data-Driven Method; Human-Robot Interaction (HRI); Reinforcement Learning; Velocity-Free
International Standard Serial Number (ISSN)
Article - Journal
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01 Jan 2022