This study examines the role of gaze and laughter coordination in human-robot interaction, focusing on how these non-verbal cues influence user perception of social robots. Using Furhat, we explore whether contextually appropriate alignment of gaze and laughter enhances the interaction quality in terms of human perception and emotional responses. Participants were divided into two experimental conditions – one experiencing well-aligned gaze and laughter, and the other encountering misaligned behaviours – while discussing a cooking activity with Furhat. Their interactions were recorded, followed by a questionnaire that assessed their perceptions and emotional responses. Results showed that participants exposed to contextually appropriate gaze-laughter alignment rated Furhat higher in empathy, naturalness and compassion compared to those who experienced the same behaviours in inappropriate contexts. Our findings suggest promising potential for designing more human-like social robots capable of meaningful non-verbal communication.

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Laughter in Sight: How Gaze and Laughter Affect Perceptions of a Social Robot

  • Eleni Giannitzi,
  • Vladislav Maraev,
  • Erik Lagerstedt,
  • Christine Howes

摘要

This study examines the role of gaze and laughter coordination in human-robot interaction, focusing on how these non-verbal cues influence user perception of social robots. Using Furhat, we explore whether contextually appropriate alignment of gaze and laughter enhances the interaction quality in terms of human perception and emotional responses. Participants were divided into two experimental conditions – one experiencing well-aligned gaze and laughter, and the other encountering misaligned behaviours – while discussing a cooking activity with Furhat. Their interactions were recorded, followed by a questionnaire that assessed their perceptions and emotional responses. Results showed that participants exposed to contextually appropriate gaze-laughter alignment rated Furhat higher in empathy, naturalness and compassion compared to those who experienced the same behaviours in inappropriate contexts. Our findings suggest promising potential for designing more human-like social robots capable of meaningful non-verbal communication.