We propose augmenting the empathetic capacities of social robots by integrating non-verbal cues. Our primary contribution is the design and labeling of four types of empathetic non-verbal cues, abbreviated as SAFE: Speech, Action (gesture), Facial expression, and Emotion, in a social robot. These cues are generated using a Large Language Model (LLM). We developed an LLM-based conversational system for the robot and assessed its alignment with social cues as defined by human counselors. Preliminary results show distinct patterns in the robot's responses, such as a preference for calm and positive social emotions like 'joy' and 'lively', and frequent nodding gestures. Despite these tendencies, our approach has led to the development of a social robot capable of context-aware and more authentic interactions. Our work lays the groundwork for future studies on human-robot interactions, emphasizing the essential role of both verbal and non-verbal cues in creating social and empathetic robots.

翻译：我们提出通过整合非语言信号来增强社会机器人的共情能力。主要贡献在于设计并标注了社会机器人中四种共情非语言信号（缩写为SAFE）：言语(Speech)、动作(手势)(Action/gesture)、面部表情(Facial expression)和情感(Emotion)。这些信号通过大型语言模型(LLM)生成。我们为机器人开发了一套基于LLM的对话系统，并评估了其与人类咨询师定义的社会信号一致性。初步结果显示机器人回应具有显著模式，例如偏好平静积极的社会情绪（如"愉悦"和"生动"），以及频繁的点头动作。尽管存在这些倾向，我们的方法仍成功开发出能够进行情境感知且更真实交互的社会机器人。本研究为人机交互领域的后续研究奠定了基础，强调了言语与非言语信号在构建具备社交与共情能力的机器人的关键作用。