Xpress: A System For Dynamic, Context-Aware Robot Facial Expressions using Language Models

2025-03-01

AI Summary

Existing robotic facial expression generation methods rely on hand-crafted animations, lacking dynamism, cross-context adaptability, and platform scalability—resulting in emotionally monotonous interactions and weak user resonance over extended engagement. This paper proposes the first large language model (LLM)-driven, three-stage framework: temporal encoding, context-conditioned modeling, and facial token generation—enabling natural, real-time, and situationally adaptive expressions. By integrating LLMs into affective robotics, our approach supports semantic understanding, dynamic temporal modeling, and multimodal contextual perception, thereby transcending the limitations of predefined animation libraries. The system employs a lightweight interface for seamless deployment across heterogeneous robotic hardware platforms. Two user studies (n=30) and a longitudinal child-family deployment (n=13) demonstrate a 68% improvement in expression dynamism, 91% contextual alignment accuracy, and significant enhancements in perceived warmth, trustworthiness, and interactional immersion.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Dynamic generation of robot facial expressions using language models.
• Improving adaptability and expressiveness in human-robot interaction.
• Enhancing interaction quality through context-aware facial expressions.

Innovation

• Leverages language models for dynamic facial expressions
• Encodes temporal flow and conditions on context
• Generates context-aware facial expression code