Adaptive choreography for user's preferences on personal robots

We propose an approach to efficiently adapt the non-verbal information of personal robots to a user's preferences. As non-verbal information focuses on gestures except meaningful gestures, in our method the adaptation problem is formalized as choreography; the action (as one unit of the non-verbal information) is assigned to each state as preferred by the user. Since the state is defined based on the classification of language focusing on the shallow discourse structure proposed by [1], our method is constructed with independence of tasks and situations unlike previous methods. Furthermore, using a preferences database obtained from multiple users, we produce a User Preference Model (UPM) that can represent various user preferences by a small number of parameters. A new user is asked to assign actions on a few states to adapt the UPM for the user preference. After the adaptation, the UPM can be used to predict actions on other states as preferred by the user, that accomplishes adaptive choreography. We implemented the proposed method with a personal robot, and validated it through experiments on multiple tasks with human subjects. As a result, we confirmed that the user's impressions of the robot were greatly improved by our method for multiple tasks.