We explore the effect of laughter perception and response in terms of engagement in human-robot interaction. We designed two distinct experiments in which the robot has two modes: laughter responsive and laughter non-responsive. In responsive mode, the robot detects laughter using a multimodal real-time laughter detection module and invokes laughter as a backchannel to users accordingly. In non-responsive mode, robot has no utilization of detection, thus provides no feedback. In the experimental design, we use a straightforward question-answer based interaction scenario using a back-projected robot head. We evaluate the interactions with objective and subjective measurements of engagement and user experience.
This paper addresses the problem of evaluating engagement of the human participant by combining verbal and nonverbal behaviour along with contextual information. This study will be carried out through four different corpora. Four different systems designed to explore essential and complementary aspects of the JOKER system in terms of paralinguistic/linguistic inputs were used for the data collection. An annotation scheme dedicated to the labeling of verbal and non-verbal behavior have been designed. From our experiment, engagement in HRI should be multifaceted.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.