Developing Social Robots with Empathetic Non-Verbal Cues Using Large Language Models
This work addresses the challenge of creating more empathetic social robots for human-robot interaction, though it appears incremental as it builds on existing LLM methods for generating non-verbal cues.
The researchers tackled the problem of enhancing social robots' empathetic capacities by integrating non-verbal cues, resulting in a robot that shows context-aware and authentic interactions with preferences for calm and positive emotions like 'joy' and frequent nodding gestures.
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.