Robot-on-Robot Gossiping to Improve Sense of Human-Robot Conversation

In recent years, a substantial amount of research has been aimed at realizing a social robot that can maintain long-term user interest. One approach is using a dialogue strategy in which the robot makes a remark based on previous dialogues with users. However, privacy problems may occur owing to private information of the user being mentioned. We propose a novel dialogue strategy whereby a robot mentions another robot in the form of gossiping. This dialogue strategy can improve the sense of conversation, which results in increased interest while avoiding the privacy issue. We examined our proposal by conducting a conversation experiment evaluated by subject impressions. The results demonstrated that the proposed method could help the robot to obtain higher evaluations. In particular, the perceived mind was improved in the Likert scale evaluation, whereas the robot empathy and intention to use were improved in the binary comparison evaluation. Our dialogue strategy may contribute to understanding the factors regarding the sense of conversation, thereby adding value to the field of human-robot interaction.