PACE: Persona Adaptation through Conversational Elicitation in Human-Robot Interaction
Researchers introduce PACE, a framework that enables humanoid robots to dynamically generate and adapt psychologically grounded personas through conversational interaction with users. The system employs an interactive elicitation pipeline to create structured identities, which are then expressed via multimodal behaviors on the Ameca robot. Empirical evaluation demonstrates that dynamically generated personas improve user trust, perceived anthropomorphism, and interaction quality compared to static, generic personas.
Why it matters: This work represents a significant advance in human-robot interaction by enabling robots to personalize their identities in real time, which can enhance trust and engagement in practical applications.
Full story at: arXiv Robotics ↗