Measurement and Prediction of Situation Awareness in Human-Robot Interaction based on a Framework of Probabilistic Attention

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Dini, A., Murko, C., Paletta, L., Yahyanejad, S., Augsdürfer, U., and Hofbaur, M.

Proc. IEEE/RSJ International Conference on Intelligent Robots and Sys-tems, IROS 2017, Vancouver, Canada, 24-28 September, 2017 , 1/2017


Human attention processes play a major role in the optimization of human-robot interaction (HRI) systems. This work describes a novel methodology to measure and predict situation awareness and from this overall performance from gaze features in real-time. The awareness about scene objects of interest is described by 3D gaze analysis using data from wearable eye tracking glasses and a precise optical tracking system. A probabilistic framework of uncertainty considers coping with measurement errors in eye and position estimation. Comprehensive experiments on HRI were conducted with typical tasks including handover in a lab based prototypical manufacturing environment. The methodology is proven to predict standard measures of situation awareness (SAGAT, SART) as well as performance in the HRI task in real-time and will open new opportunities for human factors based performance optimization in HRI applications.