Building Rapport with Self-Disclosure in Human-Robot Interaction

Abstract

As social robots become increasingly present in everyday environments, establishing rapport with users is critical for enabling sustained and meaningful human-robot interaction. In human-human communication, self-disclosure is a well-established mechanism for building rapport; however, its role and impact in human-robot interaction (HRI) are not yet well understood. Although recent advances in large language models (LLMs) have improved the linguistic competence of conversational robots, such systems are typically deployed as reactive information providers, offering limited social reciprocity and relational depth. In this article, we investigate the role of robot self-disclosure in fostering rapport during face-toface human-robot interaction. We propose a dialogue system, called RADIA that leverages retrieval-augmented generation (RAG) to enable a robot to retrieve and reference simulated autobiographical memories, allowing first-person self-disclosure to be incorporated naturally into ongoing conversations. We evaluate the proposed approach through an in-person user study, comparing interactions with a self-disclosing robot against a baseline non-self-disclosing robot. Our results show that robot self-disclosure significantly influences conversational dynamics and enhances users' perceived rapport with the robot, suggesting that self-disclosure is a valuable social behavior for long-term and socially engaging human-robot interaction.