This paper presents a method of selecting the answerer from audiences for a museum guide robot. We performed the preliminary experiments that a robot distributed its gaze towards visitors to select an answerer and analyzed visitors' responses. From these experiments, we have found that the visitors who are asked questions by the robot feel embarrassed when they have no prior knowledge about the question and the visitor's gaze during the question plays an important role to avoid being asked question. Based on these findings we developed functions for a guide robot to select the answerer by observing behaviors of multiple visitors. Multiple visitors' head motions are tracked and recognized by using an omni-directional camera and a laser range sensor. The robot detects the visitors' positive and negative responses by observing their head motions while asking questions. We confirmed the effectiveness of our method by experiments.

This paper presents a method in a museum guide robot to choose an appropriate answerer among multiple visitors. First, we observed and videotaped scenes of gallery talk when human guides ask visitors questions. Based on an analysis of this video, we have found that the guides selects an answerer by distributing his or her gaze towards multiple visitors and observing the visitors' gaze responses during the question. Then, we performed experiments on a robot that distributes its gaze towards multiple visitors, and analyzed visitors' responses. From these experiments, we found that visitors who are asked questions by the robot felt embarrassed when he or she had no prior knowledge about the questions, and that visitor gaze during the questions plays an important role in avoiding being asked questions. Based on these findings, we have developed a function in a guide robot that observes visitor gaze response and selects an appropriate answerer based on these responses. Gaze responses are tracked and recognized using an omnidirectional camera and a laser range sensor. The effectiveness of our method was confirmed through experiments.

Designing technologies that support the explanation of museum exhibits is a challenging domain. In this paper we develop an innovative approach - providing a robot guide with resources to engage visitors in an interaction about an art exhibit. We draw upon ethnographical fieldwork in an art museum, focusing on how tour guides interrelate talk and visual conduct, specifically how they ask questions of different kinds to engage and involve visitors in lengthy explanations of an exhibit. From this analysis we have developed a robot guide that can coordinate its utterances and body movement to monitor the responses of visitors to these. Detailed analysis of the interaction between the robot and visitors in an art museum suggests that such simple devices derived from the study of human interaction might be useful in engaging visitors in explanations of complex artifacts.

Nonverbal behavior plays an important role in human communication. In this paper, we propose a novel museum guide robot that has three different types of communication modes (autonomous, remote-control, and remote-interactive), which are integrated to interact with visitors through nonverbal behavior. First, the autonomous mode is an autonomously controlled mode in which the robot can directly interact with visitors through nonverbal behaviors such as head movements or arm gestures. Second, when a visitor begins to ask the robot questions, the communication is changed into a remote-control mode. In this mode, a remote operator controls the robot with gestures to interact with visitors. Third, in the remote-interactive mode, a remote operator can also interact with the robot with voice and gestures through a network. In particular, the remote operator can ask the robot to do something with nonverbal behaviors such as pointing while saying, “Bring this brochure to him.” These three communication modes are switched seamlessly depending on the situation.