Beyond Words: Enhancing Natural Interaction by Recognizing Social Conversation Contexts in HRI

摘要

With the ongoing advancements in AI technology, human-robot interactions have become increasingly prevalent, extending across diverse domains such as AI speakers and service robots. Despite the progress, users often perceive interactions with robots as lacking naturalness. One factor contributing to this perception is the improper involvement of robots in specific situations. To address these issues, this paper proposes a method for defining and recognizing social conversation contexts. Furthermore, the paper outlines plan for constructing a database to assess the performance of the defined problem. By enabling robots to recognize social conversational situations based on the speaker and addressee and generate context-aware actions, we envision achieving more natural interactions. Through the newly proposed situation definition and problem-solving approach, we anticipate alleviating some of the unnatural interaction elements in Human-Robot Interaction (HRI) scenarios.