  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.
