Decoding Engagement: The Role of Closeness Cues in Human-Robot Interactions

This work examines the critical role of nonverbal cues in initiating successful human-robot interactions (HRI), focusing on the development of an algorithm capable of decoding subtle cues that indicate a human's intention to interact with a robot. The foundation for the analysis is the concept of closeness which relates to the individual feeling of intimacy and comfort, and indicates the engagement potential in diverse settings and interactions. This paper introduces a novel algorithm designed to quantify a person's willingness to interact based on analyzing the displayed closeness on a series of images. The algorithm's efficacy is evaluated through an empirical study involving 20 participants, generating a substantial dataset of 130 video sequences. These sequences were analyzed to assess the algorithm's ability to predict communication initiation intentions, with findings suggesting a significant distinction between participants engaged in interaction with a robot and those instructed to ignore it. The results underscore the algorithm's potential in facilitating more nuanced and socially sustainable HRI, laying the groundwork for future advancements in the field. This research contributes to the growing body of work on social robotics, emphasizing the importance of integrating nonverbal cue analysis for enhancing robots' interactive capabilities and fostering more meaningful human-robot connections.