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.
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ASME 2017 Dynamic Systems and Control Conference
October 11–13, 2017
Tysons, Virginia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5827-1
PROCEEDINGS PAPER
Towards Understanding Human Decisions in Human-Robot Interactions Available to Purchase
Wenlong Zhang,
Wenlong Zhang
Arizona State University, Mesa, AZ
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Yezhou Yang,
Yezhou Yang
Arizona State University, Tempe, AZ
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Yi Ren
Yi Ren
Arizona State University, Tempe, AZ
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Wenlong Zhang
Arizona State University, Mesa, AZ
Yezhou Yang
Arizona State University, Tempe, AZ
Yi Ren
Arizona State University, Tempe, AZ
Paper No:
DSCC2017-5290, V001T30A009; 10 pages
Published Online:
November 14, 2017
Citation
Zhang, W, Yang, Y, & Ren, Y. "Towards Understanding Human Decisions in Human-Robot Interactions." Proceedings of the 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. V001T30A009. ASME. https://doi.org/10.1115/DSCC2017-5290
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