2025/03/01 by Victor Nikhil Antony, Antony, Victor Nikhil, Maia Stiber +3 · 1 citation
Psychology · #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Robotics (cs.RO) #Social Robot Interaction and HRI
paper · pdf · doi:10.48550/arxiv.2503.00283
openalex publication_date 2025/03/01 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28
Facial expressions are vital in human communication and significantly influence outcomes in human-robot interaction (HRI), such as likeability, trust, and companionship. However, current methods for generating robotic facial expressions are often labor-intensive, lack adaptability across contexts and platforms, and have limited expressive ranges--leading to repetitive behaviors that reduce interaction quality, particularly in long-term scenarios. We introduce Xpress, a system that leverages language models (LMs) to dynamically generate context-aware facial expressions for robots through a three-phase process: encoding temporal flow, conditioning expressions on context, and generating facial expression code. We demonstrated Xpress as a proof-of-concept through two user studies (n=15x2) and a case study with children and parents (n=13), in storytelling and conversational scenarios to assess the system's context-awareness, expressiveness, and dynamism. Results demonstrate Xpress's ability to dynamically produce expressive and contextually appropriate facial expressions, highlighting its versatility and potential in HRI applications.