Realizing Robust Human-Robot Interaction under Real Environments with Noises

Abstract

A human speaker considers her interlocutor's situation when she determines to begin speaking in human-human interaction. We assume this tendency is also applicable to human-robot interaction when a human treats a humanoid robot as a social being and behaves as a cooperative user. As a part of this social norm, we have built a model of predicting when a user is likely to begin speaking to a humanoid robot. This proposed model can be used to prevent a robot from generating erroneous reactions by ignoring input noises. In my Ph.D. thesis, we will realize robust human-robot interaction under real environments with noises. To achieve this, we began constructing a robot dialogue system using multiple modalities, such as audio and visual, and the robot's posture information. We plan to: 1) construct a robot dialogue system, 2) develop systems using social norms, such as an input sound classifier, controlling user's untimely utterances, and estimating user's degree of urgency, and 3) extend it from a one-to-one dialogue system to a multi-party one.