Facilitating Co-regulation: An LLM-Powered Multimodal Social Robot for Parent-Child Dyads

Parental involvement is crucial for the development of children's emotion regulation (ER) skills, yet navigating these complex emotional interactions remains challenging for many families. While Large Language Models (LLMs) offer unprecedented conversational flexibility, integrating them into embodied social robots to provide context-aware, multimodal support remains an open challenge. In this paper, we present the design and preliminary evaluation of an LLM-powered robotic system aimed at facilitating ER within parent-child dyads. Utilizing a supervised autonomy approach, our system bridges the gap between language-based reasoning and embodied robotic behavior, allowing the MiRo-E robot to engage in natural dialogue while performing empathetic physical actions. We detail the system's technical architecture and interaction design, which guides dyads through evidence-based ER strategies. Preliminary user tests with six parent-child dyads suggest positive user engagement and initial trust, with participants reporting that the robot showed potential as a supportive mediator. These findings offer early design insights into developing autonomous, LLM-driven social robots for family-centered mental health interventions.