Design Implications for Effective Robot Gaze Behaviors in Multiparty Interactions

ACM/IEEE International Conference on Human-Robot Interaction(2022)

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ABSTRACTHuman-robot non-verbal communication has been a growing focus of research, as we realize its importance to achieve interaction goals (e.g. modulating turn-taking) and manage human perception of the interaction. Consequently, the development of models for robot non-verbal behavior, such as gaze, should be informed by studies of human reaction and perception to that behavior. Here, we look at data from two studies where two humans interact describing words to a robot. The robot tries to balance participation of the two players through a combination of gaze aversion, looking at the listener and looking at the speaker. We analyze how momentary gaze patterns reflect in the participant's turn length and perception of the robot, as well as in the participation imbalance. Our findings may be used as recommendations towards crafting robot gaze behaviors in multiparty interactions.
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