An Autonomous Conversational Android that Acquires Human–Item Co-Occurrence in the Real World

Abstract

The goal of this study is to develop an autonomous conversational robot that acquires knowledge about human society in the real world. In this study, we define experience data as image data obtained from a camera (visual information) and dialogue data obtained from a microphone (auditory information). From experience data, the robot acquires knowledge about the co-occurrence between humans and items, that is, whether it is usual or not for humans to have the items. Not only does the proposed system acquire knowledge, but also generates utterances based on acquired knowledge. We have implemented the proposed method on an android, a human-like robot. We conducted an experiment in which the android was placed in a real-world environment (shopping mall) and interacted with visitors. The percentage of positive responses to the robot's questions based on the acquired knowledge suggests that this system can acquire knowledge from experience data.