In this paper, we propose a robot system which can take visitors on guided tours in a museum. When we consider developing a robot capable of giving a tour of an actual museum, we must implement a robot system able to measure the location and orientation of the robot and visitors using bearing sensors installed in a specific environment. Although many previous methods employed markers or tags attached to the robot to obtain positional information through cameras, it is not easy to situate sensors in the environment itself. SLAM is also used to localize the position of the robot. However, its robustness may not be sufficient when the system is deployed in an actual real-life situation since there will be many people moving freely around the robot. On the other hand, the robot needs information pertaining to both tour attendees and other visitors in the vicinity, in order to give a tour for the attendees. Therefore, we propose a new robot system which consists of simple devices such as multiple laser range finders attached to a pole. By just placing the sensor poles, we could track the location and orientation of the robot and visitors at the same time. We then conducted experiments to confirm the effectiveness and accuracy of our system. In addition, we conducted demonstrative experiments that our robot takes three visitors to the guide tour.

From our ethnographic studies on various kinds of museums, we discovered that guides routinely propose questions to visitors in order to draw their attention towards both his/her explanation and the exhibit. The guides' question sequences tend to begin with a pre-question which serves to not only monitor visitors' behavior and responses, but to also alert visitors that a primary question would follow. We implemented this questioning-strategy with our robot system and investigated whether this strategy would also work in human-robot interaction. We developed a vision system that enables the robot to choose an appropriate visitor by monitoring a visitor's response from the initiation of a pre-question to the following pause. Results indicate that this questioning-strategy works effectively in human-robot interaction. In this experiment, the robot asked visitors about a photograph. At the pre-question, the robot delivered a rather easy question followed by a more challenging question (Figure 1). More participants turned their head away from the exhibition when they were not sure about their answer to the question. They either faced away from the robot, or smiled wryly at the robot or at each other. These types of behaviors index participants' states of knowledge, which we could utilize to develop a system by which the robot could choose an appropriate candidate by computational recognition.

In this paper, we present our work designing a robot that explains an exhibit to multiple visitors in a museum setting, based on ethnographic analysis of interactions between expert human guides and visitors. During the ethnographic analysis, we discovered that expert human guides employ some identical strategies and practices in their explanations. In particular, one of these is to involve all visitors by posing a question to an appropriate visitor among them, which we call the "creating a puzzle" sequence. This is done in order to draw visitors' attention towards not only the exhibit and but also the guide's explanation. While creating a puzzle, the human guide can monitor visitors' responses and choose an "appropriate" visitor (i.e. one who is likely to provide an answer). Based on these findings, sociologists and engineers together developed a guide robot that coordinates verbal and non-verbal actions in posing a question or "a puzzle" that will draw visitors' attention, and then explain the exhibit for multiple visitors. During the explanation, the robot chooses an "appropriate" visitor. We tested the robot at an actual museum. The results show that our robot increases visitors' engagement and interaction with the guide, as well as interaction and engagement among visitors.