用语言模型让机器人面部表情实时适配对话上下文
Xpress: A System For Dynamic, Context-Aware Robot Facial Expressions using Language Models
- 通过三阶段流程，将对话内容转为动态表情代码
- 用户研究显示表情自然且符合语境，提升交互体验
- 适合长期陪伴型机器人，尤其儿童互动场景
面部表情在人机交互中至关重要，影响好感度、信任感和陪伴感。现有机器人表情生成方法多依赖人工设计，适应性差、表达范围有限，易导致重复行为，降低长期交互质量。我们提出Xpress系统，利用语言模型实现动态、上下文感知的面部表情生成，包含时序编码、上下文条件化和表情代码生成三个阶段。通过两轮用户研究（每轮n=15）和一个亲子案例研究（n=13），在讲故事与对话场景中验证了系统的上下文感知能力、表现力与动态性。结果表明，Xpress能有效生成贴合语境的生动表情，展现其在人机交互中的广泛潜力。
原文摘要 · Abstract (English)
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.
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