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.

In this paper, we report a model that allows a robot to appropriately control its position as it presents information to a user. This capability is indispensable, since in the future, many robots will function in daily situations such as shopkeepers presenting products to customers or museum guides presenting information to visitors. Psychology research suggests that people adjust their positions to establish a joint view toward a target object. Similarly, when a robot presents an object, it should stand at an appropriate position that considers the positions of both the listener and the object to optimize the listener's field of view and establish a joint view. We observed human-human interaction situations, where people presented objects, and developed a model for an information-presenting robot to appropriately adjust its position. Our model consists of four constraints to establish O-space: 1) proximity to listener; 2) proximity to object; 3) listener's field of view; and 4) presenter's field of view. We also experimentally evaluate the effectiveness of our model.

Human communication involves a number of nonverbal cues that are seemingly unintentional, unconscious, and automatic-both in their production and perception-and convey rich information on the emotional state and intentions of an individual. One family of such cues is called "nonverbal leakage." In this paper, we explore whether people can read nonverbal leakage cues-particularly gaze cues-in humanlike robots and make inferences on robots' intentions, and whether the physical design of the robot affects these inferences. We designed a gaze cue for Geminoid-a highly humanlike android-and Robovie-a robot with stylized, abstract humanlike features-that allowed the robots to "leak" information on what they might have in mind. In a controlled laboratory experiment, we asked participants to play a game of guessing with either of the robots and evaluated how the gaze cue affected participants' task performance. We found that the gaze cue did, in fact, lead to better performance, from which we infer that the cue led to attributions of mental states and intentionality. Our results have implications for robot design, particularly for designing expression of intentionality, and for our understanding of how people respond to human social cues when they are enacted by robots.

In this paper, we present our current research on developing a model of robot behavior that leads to feelings of being together using the robot's body position and orientation. Creating feelings of"being together"will be an essential skill for robots that live with humans and adapt to daily human activities such as walking together or establishing joint attention to information in the environment. We observe people's proxemic behavior in joint attention situations and develop a model of behavior for robots to detect a partner's attention shift and appropriately adjust its body position and orientation in establishing joint attention with the partner. We experimentally evaluate the effectiveness of our model, and our results demonstrate the model's effectiveness.

This paper describes a model for a robot to appropriately control its position when it presents information to a user. This capability is indispensable since in the future many robots will be functioning in daily situations as shopkeepers presenting products to customers or museum guides presenting information to visitors. Psychology research suggests that people adjust their positions to establish a joint view toward a target object. Similarly, when a robot presents an object, it should stand at an appropriate position that considers the positions of both the listener and the object to optimize the listener's field of view and to establish a joint view. We observed human-human interaction situations where people presented objects and developed a model for an information-presenting robot to appropriately adjust its position. Our model consists of four constraints for establishing O-space: (1) proximity to listener, (2) proximity to object, (3) listener's field of view, and (4) presenter's field of view. We also present an experimental evaluation of the effectiveness of our model.

Recently, remote-controlled communication robots have been studied. Our question is whether the person interacting with them feels that he/she is interacting with the robot itself or the human behind it. How do such beliefs affect interaction? We conducted an experiment to study the effect of a human presence behind an interactive humanoid robot. We used an interactive robot system based on a motion-capturing system where participants can bodily interact with the robot. They were deceptively informed that they would either be interacting with a robot controlled by a program or a human; in fact, in both conditions the robot was autonomous. Experimental results indicated that two-thirds of the participants felt that they were interacting with the robot itself. Their enjoyment was unaffected by the knowledge of whether the robot was controlled by a program or a human, although their impression of robot intelligence indicated that they distinguished between these conditions. On the contrary, since the remaining one-third of the participants felt they were interacting with a human behind the robot, they were affected by the knowledge: when they were told they were interacting with a teleoperated robot, they enjoyed it less; in contrast, their enjoyment increased when they were told they were interacting with an autonomous robot.

We believe that lifelikeness is important to make it possible for a communication robot to interact naturally with people. In this research, the relationship between contingency and complexity in a lifelike robot was investigated. We have developed a robot control system to control experimentally contingency and complexity in interaction by combining a humanoid robot with a motion-capturing system. Two independent experiments were conducted with different levels of interaction complexity: simple interaction and complex interaction. Experiments in the simple interaction situation indicated that subjects felt that the robot behaved autonomously when contingency was low, and there was no significant relationship between contingency and a lifelike impression. On the other hand, experiments in the complex interaction situation showed that the robot gave the subjects the impression of being more autonomous and lifelike when contingency was high.

The purpose of our research is to develop lifelike behavior in a communication robot, which is expected to potentially make human-robot interaction more natural. Our earlier research demonstrated the importance of a robot's contingency for lifelikeness [1]. On the other hand, perfect contingency seems to give us a non-lifelike impression. In order to explore the appropriate contingency for communication robots, we developed a robot system that allows us to adjust its contingency to an interacting person in a simple mimic interaction. As a result of an experiment, we identified the relationships between the degree of contingency and the subjective impressions of lifelikeness, autonomy, and preference. However, the experimental result also seems to suggest the importance of the complexity of interaction for investigating the appropriate contingency of communication robots.