The Power of Atmosphere: LLM-Based Social Task Generation of Robots

In Human-Robot Interaction (HRI), the ability of robots to understand social atmosphere is essential for the quality improvement of the interaction. However, robots have limitations in recognizing and performing appropriate behaviors for social atmosphere that are not externally revealed. In this study, based on Large Language Models (LLMs), we aimed to investigate whether adding atmosphere elements to robots' social behavior generation could enable robots to generate and perform more appropriate behaviors in social contexts. A total of 50 participants participated in the experiment. As a result of the experiment, a robot scenario that incorporates atmospheric elements showed significantly higher differences in terms of the robot's social behavior, sociability, and human-robot interaction, compared to a robot scenario without atmospheric elements. This study provides new insights into the importance of considering social context when setting prompting elements for LLM-based social robots. By adding social atmospheric elements, it is expected that robots will be able to improve human's understanding of social context and perform more natural and effective interactions.