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Press Start to Play: Classifying Multi-Robot Operators and Predicting Their Strategies through a Videogame

1
Centre for Automation and Robotics (UPM-CSIC), Technical University of Madrid, José Gutiérrez Abascal, 2, 28006 Madrid, Spain
2
Computer Vision & Artificial Intelligence Group, Technical University of Munich, Boltzmannstrasse 3, 85748 Garching, Germany
3
Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
4
Inria, Université Grenoble Alpes, Grenoble INP, 38000 Grenoble, France
*
Author to whom correspondence should be addressed.
Robotics 2019, 8(3), 53; https://doi.org/10.3390/robotics8030053
Received: 23 May 2019 / Revised: 28 June 2019 / Accepted: 8 July 2019 / Published: 9 July 2019
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Abstract

One of the active challenges in multi-robot missions is related to managing operator workload and situational awareness. Currently, the operators are trained to use interfaces, but in the near future this can be turned inside out: the interfaces will adapt to operators so as to facilitate their tasks. To this end, the interfaces should manage models of operators and adapt the information to their states and preferences. This work proposes a videogame-based approach to classify operator behavior and predict their actions in order to improve teleoperated multi-robot missions. First, groups of operators are generated according to their strategies by means of clustering algorithms. Second, the operators’ strategies are predicted, taking into account their models. Multiple information sources and modeling methods are used to determine the approach that maximizes the mission goal. The results demonstrate that predictions based on previous data from single operators increase the probability of success in teleoperated multi-robot missions by 19%, whereas predictions based on operator clusters increase this probability of success by 28%. View Full-Text
Keywords: robotics; multi-robot mission; operator; modeling, clustering; prediction; adaptive interface; situational awareness; workload robotics; multi-robot mission; operator; modeling, clustering; prediction; adaptive interface; situational awareness; workload
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Roldán, J.J.; Díaz-Maroto, V.; Real, J.; Palafox, P.R.; Valente, J.; Garzón, M.; Barrientos, A. Press Start to Play: Classifying Multi-Robot Operators and Predicting Their Strategies through a Videogame. Robotics 2019, 8, 53.

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