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Towards Understanding Human Decisions in Human-Robot Interactions

[+] Author Affiliations
Wenlong Zhang

Arizona State University, Mesa, AZ

Yezhou Yang, Yi Ren

Arizona State University, Tempe, AZ

Paper No. DSCC2017-5290, pp. V001T30A009; 10 pages
doi:10.1115/DSCC2017-5290
From:
  • ASME 2017 Dynamic Systems and Control Conference
  • Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5827-1
  • Copyright © 2017 by ASME

abstract

In this paper, we conducted an experiment with four human participants whom were asked to follow a robot gripper with unknown motion as close as possible. The results show that human beings resort to a fairly complicated and continuously changing control strategy. We hypothesize that this strategy can be explained by (1) a feedforward (preview) model of the machine’s motion, and further by (2) human being’s uncertainty in this preview. To test (1), we demonstrate that feedforward control can indeed improve the fitting of the model to the experimental data, and that the feedback gain and the preview length vary across subjects. This model, however, does not explain temporally changing human behavior observed during the experiment. To this end, we propose an extension of the human control model where human behavior is influenced by the preview uncertainty. The extended model incorporates a higher-level planner that determines a target state for a short time interval, and a lower-level controller that meets the target through real-time control. The developed model helps predict detailed human behavior during their interactions with robots.

Copyright © 2017 by ASME
Topics: Robots

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