Research on Robots that Recognize Surrounding Environments and Adhere to Social Manners

Abstract

In this paper, we propose the Judging Manner Interaction System (Jmis), an interaction system that employs large language models (LLMs) to evaluate manners based on environmental information, with the aim of enabling robots to behave with manners and demonstrate flexible, human-considerate actions. In shared spaces between humans and robots, it is essential for robots to actively account for the presence of humans, act with flexibility, and be capable of judging given instructions from an ethical perspective, including refusing inappropriate ones. By integrating Jmis into robots, the system’s Manner Judgment Module and Manner Improvement Module allow robots to embody manners and adopt considerate, flexible behaviors. The Manner Judgment Module uses an LLM to assess whether the robot’s planned actions (goals and the actions taken to achieve them) comply with social manners. Furthermore, in the Manner Improvement Module, when actions are judged as violating manners, the system employs an LLM to determine whether manners can be improved through communication with surrounding humans, and it generates appropriate utterances. To validate the effectiveness of the Manner Judgment and Manner Improvement Modules of Jmis, we conducted evaluation experiments examining impressions of robot behaviors with Jmis implemented. The results showed that, in both simulated and real environments, robots using Jmis gave participants stronger impressions of likability and perceived intelligence. Moreover, it was demonstrated that robots equipped with manners conveyed the intentions behind their actions more clearly.