Creating and sustaining rapport between robots and people is critical for successful robotic services. As a first step towards this goal, we explored a personalization strategy with a snack delivery robot. We designed a social robotic snack delivery service, and, for half of the participants, personalized the service based on participants ’ service usage and interactions with the robot. The service ran for each participant for two months. We evaluated this strategy during a 4-month field experiment. The results show that, as compared with the social service alone, adding personalized service improved rapport, cooperation, and engagement with the robot during service encounters.

Abstract — We present a system to learn task representations from ambiguous feedback. We consider an inverse reinforcement learner that receives feedback from a user with an unknown and noisy protocol. The system needs to estimate simultaneously what the task is, and how the user is providing the feedback. We further explore the problem of ambiguous protocols by considering that the words used by the teacher have an unknown relation with the action and meaning expected by the robot. This allows the system to start with a set of known symbols and learn the meaning of new ones. We present computational results that show that it is possible to learn the task under a noisy and ambiguous feedback. Using an active learning approach, the system is able to reduce the length of the training period. I.