Engagement-Based Adaptive Behaviors for Laboratory Guide in Human-Robot Dialogue

Koji Inoue, Divesh Lala, Kenta Yamamoto, Katsuya Takanashi, Tatsuya Kawahara

Abstract: We address an application of engagement recognition in human-robot dialogue. Engagement is defined as how much a user is interested in the current dialogue, and keeping users engaged is important for spoken dialogue systems. In this study, we apply a real-time engagement recognition model to laboratory guide by autonomous android ERICA which plays the role of the guide. According to an engagement score of a user, ERICA generates adaptive behaviors consisting of feedback utterances and additional explanations. A subject experiment showed that the adaptive behaviors increased both the engagement score and related subjective scores such as interest and empathy.