Decision-Making Prediction For Human-Robot Engagement Between Pedestrian And Robot Receptionist

Social robots have been providing a number of customer services lately. For example, they can take the place of people as receptionists. When people interact they predict each others decision-making from the others actions and take suitable actions for the sake of the other. However, taking such actions is difficult for modern social robots. Therefore, our initial aim is to solve the problem of how to predict decision-making in human-robot engagement. Choosing a reception system as a case study to approach this goal, we created a new model to predict who will use the system. The model contains a state transition function based on observation studies. We evaluated the model though a controlled experiment and a simulation experiment, using field data on prediction performance and mental effect. The experiment results lead us to believe that the model can predict the decisions of others sufficiently well. We also found that a pedestrian who will not talk with a robot receptionist suffers a negative emotion when the pedestrian is greeted by a robot, but a method we propose prevents this. We suggest that the word suitable is related to negative emotion, a part of usability. By using our model in the future we will attempt to find a method that will enable a robot to learn suitable actions by itself.