We used a social robot as a teaching assistant in a class for children's collaborative learning. In the class, a group of 6th graders learned together using Lego Mindstorms. The class consisted of seven lessons with Robovie, a social robot, followed by one lesson to test their individual achievement. Robovie managed the class and explained how to use Lego Mindstorms. In addition to such basic management behaviors for the class, we prepared social behaviors for building relationships with the children and encouraging them. The result shows that the social behavior encouraged children to work more in the first two lessons, but did not affect them in later lessons. On the other hand, social behavior contributed to building relationships and attaining better social acceptance.

This paper presents a development framework for social robots with which developers can easily prepare communicative behaviors based on tag-based sentences. Previous literature in human-robot interaction has revealed various useful non-verbal behaviors. But for developers, integrating such a large amount of non-verbal behaviors each time they build a social robot is not realistic. The more repertory of non-verbal behaviors acquire, the larger is the burden faced by developers to implement the non-verbal behaviors. Our software, however, only requires developers to prepare sentences with simple markup language for controlling explicit non-verbal behaviors and utterances. The backend system analyzes the tag input and adds implicit non-verbal behaviors accordingly. With the help of previous literature, the system is equipped with various communicative behaviors, so that the robot autonomously adjusts its gazes, gestures, and standing points. The system's effectiveness is demonstrated with examples on robot interaction where the system receives only simple sentences with scripting language to produce complex and communicative robot behaviors.

How humans recognize others is a central topic in both psychology and the field of robot communication. In this paper, we address the issue of such social cognition in a triadic situation since it has not yet been well explored in psychological studies although it is not negligible in daily situations. Classical Heider's balance theory has been suggested in that there are balanced states of triadic relationships that are perceived as comfortable by persons who are involved in them. However, whether, or how, such balanced states tend to be formed has not been well explored. In this paper, we investigate what we call 'social balancing effect' of synchronized nonverbal behavior: social relationships can be attracted to a balanced state due to the synchronized behavior between two agents. In this paper, we conduct an experiment in a conversational interaction among two humans and an android and report that observing eye contacts between the robot and a confederate affected the subject's recognition of the android as attracted to a balanced state.

Communication skills are necessary for robots to get by in daily life. Previous studies have shown that the chameleon effect, which increases the likeability of a person who mimicks another during a conversation, appears not only with humans but also with CG agents. However, if we use humans, they cannot accurately mimic. In addition, if we use a CG agent, it doesnpsilat have a very humanlike appearance. It remains doubtful whether the CG agent reappears in a human-human communication. Thus, we use an android with a very humanlike appearance and approach the principle of human-human communication. In this paper, we compared mimic and non-mimic conditions when an android conversed with subjects in face-to-face. Likeability toward an android increased when the android mimicked its partner. In addition, we confirmed the possibility of using an android to explore human-human communication. These results contribute to verbal communication for robots in daily life.

The "uncanny valley" must be avoided from the viewpoint of communication robot design, while this is an essential phenomenon for discovering the principles relevant to establishing social interaction between human and robot. Studying the uncanny valley allows us to explore the boundary of human-likeness. We have empirically and experimentally obtained evidence to show the uncanny valley. We have also obtained experimental evidence to suggest that the uncanny valley varies owing to the development of individuals. We refer to this variable uncanny valley as the age-dependent uncanny valley. We assume that the uncanny valley is induced by a lateral inhibition effect, and is referred to herein as the lateral inhibition hypothesis of the uncanny valley;. The present paper presents evidence concerning the uncanny valley and describes the likelihood of the present hypothesis.