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2014
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(2014) : Personalizing robot behavior for interruption in social human-robot interaction: 2014 IEEE International Workshop on Advanced Robotics and its Social Impacts: Evanston, IL: IEEE, S. 44-49
DOI: https://doi.org/10.1109/ARSO.2014.7020978 Abstract: People engaging in an activity usually has individual tolerance to be interrupted [1], [2]. Humans subconsciously adapt their behaviors to draw other one’s attention and to get into a conversation based on their historical experiences, but robots often fail to be aware of humans’ feeling and thus interrupt their users repeatedly. To endow service robots with such socially acceptable ability, we propose an online human-aware interactive learning framework in this paper, under which the robot personalizes its behaviors according to both observed user’s attention and its conjecture about user’s awareness of itself. To this purpose, the correlation between the robot’s theory of awareness, user’s attention and robot behavior are explored through reinforcement learning techniques. The conducted experiment shows that the robot can personalize its interruption strategy, and the optimal policies converged for at least 26 episodes.
Keywords: Face, Hidden Markov models, human-robot interaction, ieee xplore, Interrupters, interruption strategy, Künstliche Intelligenz, learning (artificial intelligence), Markov processes, online human-aware interactive learning framework, reinforcement learning techniques, robot behavior personalization, Robot sensing systems, robot theory of awareness, service robot, social human-robot interaction, social sciences, user attention, user awareness
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