Gamification for Personalized Human-Robot Interaction in Companion Social Robots

Abstract

Over the past two decades, companion social robots have been employed to assist humans in various environments such as healthcare, entertainment, and assistive technology. Social robots can assist healthcare professionals with psychosocial interventions and personalized digital assistance, particularly in home-care settings where on-site support is limited. To serve effectively, these robots must adapt their interactions with users to be more likable and engaging to them. To investigate this, we propose a novel interaction framework comprising a human participant, a social robot, and a digital task, enhanced with gamification elements. Integrating game-like elements such as points, levels, and feedback loops can motivate users to interact more frequently and deeply with robots, thus improving the interaction experience. Two main studies were conducted using the proposed setup, which incorporates gamification elements like textual, verbal and visual reinforcing feedback, challenge levels, performance feedback, and affect-based feedback. The aim is to contribute to the engagement studies in Human-Robot Interaction (HRI) and adapt the interaction using the gamification elements based on engagement detection models to maintain and enhance user engagement. Future studies may explore integrating role-playing scenarios, instructional tips, and emotional agents to further refine this approach, in addition to conducting longitudinal studies to validate users' long-term engagement.