Abstract
In recent years, advancements in Unmanned Systems have allowed Human Robot Interaction (HRI) to transition from direct remote control to autonomous systems capable of self-navigation. However, these new technologies do not yet support true mixed-initiative solider-robot teaming where soldiers work with another agent as if it were another human being. In order to achieve this goal, researchers must explore new types of multi-modal and natural communication strategies and methods to provide robots improved understanding of their human counterparts’ thought process. Physiological sensors are continuously becoming more portable and affordable leading to the possibility of providing new insight of team member state to a robot team member. However, steps need to be taken to improve how affective and cognitive states are measured and how these new metrics can be used to augment the decision making process for a robot team member. This paper describes current state of the art and next steps needed for accurate profile creation for improved human robot team performance.
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DARPA: URBAN Challenge. In: DARPA. HYPERLINK, http://www.darpa.mil/grandchallenge/index.asp (accessed February 3, 2011)
Hearst, M., Allen, J., Guinn, C., Horvitz, E.: Mixed-Initiative Interaction: Trends & Controversies. IEEE Intelligent Systems, 14–23 (1999)
Barnes, M., Jentsch, F. (eds.): Human-Robot Interactions in Future Military Operations. Ashgate (2010)
Grey, A., Redden, E., Coovert, M., Elliot, L.: Empowering followers in virtual teams: Guiding principles from theory and practice. Computers in Human Behavior 24, 1884–1906 (2008)
Cosenzo, K., Capstick, E., Pomranky, R., Dungrani, S., Johnson, T.: Soldier Machine Interface for Vehicle Formations: Interface Design and an Approach Evaluation and Experimentation. Technical Report ATRL-TR-4678, Aberdeen Proving Ground (2009)
Pettitt, R.A., Carsten, E.S.R., Scalablity, C.B.: of Robotic Controllers: Speech-based Robotic Controller Evaluation (ARL-TR-4858). Aberdeen Proving Ground, MD: US Army Research Laboratory, 1-46 (2009)
Kruegar, G.P.: Sustained work, fatigue, sleep loss, and performance: A review of the issues. Work & Stress, 129–141 (1989)
Bradley, M., Miccoli, L., Escrig, M., Lang, P.: The pupil diameter as a measure of emotional arousal and autonomic aviation. Psychophysiology 45, 602–607 (2008)
Marshall, S.: The index of measuring cognitive workload. IEEE 7th Human Factors Meeting, Scottsdale, AZ, 7.5–7.9(2002)
Berka, C., Levendowski, D., Lumicao, M.A.Y., Davis, G., Zivkovic, V., Olmstead, R., Tremoulet, P., Craven, P.: EEG correlates of engagement and mental workload in vigilance, learning, and memory tasks. Aviation, Space, and Environmental Medicine 78, B231–B244 (2007)
Camilli, M.: ASTEF: A simple tool for examining fixations. Behavior Research Methods, 373–382 (2008)
Rani, P., Sims, J., Brackin, R., Nilanjan, S.: Online stress detection using psychophysiological signals for implicit human-robot coorperation. Robotica, 673–685 (2002)
Rani, P., Sarkar, N.: Operator Engagement Detection and Robot Behavior Adaptation in Human-Robot Interaction. In: IEEE International Conference on Robotics and Automation, pp. 2051–2056 (2005)
Rani, P., Sarkar, N., Smith, C., Kirby, L.: Anxiety Detecting Robotic System-Towards Implicit Human-Robot Collaboration. Robotica 22, 85–95 (2004)
Hart, S., Wickens, C.: Workload assessment and prediction. In: Booher, H.R. (ed.) MANPRINT: An approach to systems integration. Van Nostrand Reinhold, New York (1990)
Wickens, C.D.: Multiple resources and performance prediction, pp. 159–177 (2002)
Kramer, A.: Physiological measures of workload: A review of recent progress. In: Damos, D. (ed.) Multiple Task Performance. Taylor and Francis, London (1991)
Parasuraman, R.: Event-related brain potentials and human factors research. In: Rohrbaugh, J., Parasuraman, R., Johnson, R. (eds.) Event-related Brain Potentials: Basic and Applied Issues. Oxford University Press, New York (1990)
McCarley, J.S., Kramer, A.F.: Cerebral hemodynamics and vigilance performance. In: Parasuraman, R., Rizzo, A.M. (eds.) Neuroergonimcs: The Brain at Work, pp. 95–112. MIT Press, Cambridge (2007)
Clark, P.J., Evans, F.C.: Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445–453 (1954)
Di Nocera, F., Camilli, M., Terenzi, M., Nacchia, R.: Cognitive aspects and behavioral effects of transitions between levels of automation. Technical Report FA8655-05-1-3021, EOARD (2007)
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Barber, D., Reinerman-Jones, L., Lackey, S., Hudson, I. (2011). Augmenting Robot Behaviors Using Physiological Measures. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. Directing the Future of Adaptive Systems. FAC 2011. Lecture Notes in Computer Science(), vol 6780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21852-1_65
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