We have studied a decision model of the conversion tasks scheduling for robots. Our decision model is essential for communication robots in public spaces to handle interruptions from outside the conversation.
本研究では、看護職員が不足している時に、被介護者の話し相手になるロボットを作成する。 表情認識を使い、被介護者のメンタルサインを読み取り、それを考慮した話題をクラウド上にある介護ログを用いて生成する。
Since lack of variety of personalities may cause unnatural communication with robots, we propose C^2AT^2 HUB where robots are characterized through long-term interaction. This method defines robot affect as “emotion” and “interpersonal affect” and characterizes robots gradually by adjusting tendency of affect based on history of user actions to robots.
in this research, we propose a human robot communication method in which the robot judges the reaction of the opponent in stages, decides the appropriate intervals according to the spot, and the robot shrinks or separates the distance himself. The robot himself measured the intersection from the expression and behavior of the opponent and designed and evaluated a service that allows the user to take a distance that allows the user to feel confident and comfortable communication.