Optimized Assistive Human-Robot Interaction using Reinforcement Learning

Abstract

An intelligent human-robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human-robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot's dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an x-y table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Adaptive Control Systems; Controllers; Intelligent Robots; Man Machine Systems; Reinforcement Learning; Robots; Closed-Loop Behavior; Human Factor Studies; Human Robot Interaction (HRI); Human-Robot Systems; Impedance-Based Control; Linear Quadratic Regulator; Neuro-Adaptive Controllers; Outer-Loop Controller; Human Robot Interaction; Artificial Intelligence; Biological Model; Computer Simulation; Cybernetics; Human; Procedures; Robotics; Task Performance; Humans; Models; Neurological; Task Performance and Analysis; Adaptive Impedance Control; Human-Robot Interaction (HRI); Neuro-Adaptive Control; Reinforcement Learning (RL)

International Standard Serial Number (ISSN)

2168-2267

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Mar 2016

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