Integrating passive RFID tag and person tracking for social interaction in daily life

This paper reports a method in which a communicative robot simultaneously interacts with two persons and identifies them with passive-type RFID and floor sensors. The difficulty emanates from the association of these inputs. A passive-type RFID reader provides very accurate person identification. However, the robot cannot specify who actually used the RFID if surrounded by many people. A floor sensor provides robust tracking of peoplepsilas positions, but it does not identify any person by itself. Thus, combining these sensors into one device that provides robust tracking of position with accurate person identification would be very beneficial. Toward this problem, in our key idea, the robot keeps multiple hypotheses about the interpretation of sensor information and gradually improves their accuracy through interaction with the people. The multimodal inputs from sensors are integrated with a Bayesian network that allows us to exploit peoplepsilas behavior patterns as parameters to estimate the accuracy of our hypotheses. Our method also considers the vagueness of current hypotheses when choosing appropriate interactive behaviors. When the robot has reservations about the situation, it prefers non-conclusive behavior and tries to solve the ambiguity by performing verification behaviors. Once the robot is certain, it prefers conclusive behavior. The developed system was tested in a real daily environment, a shopping center, where we gathered interaction data between the robot and multiple people as teaching data for the Bayesian network. The experimental results revealed that the system successfully identified 79.2% of the visitors. In particular, the hypothesis-based system for behavior control improved the successful rate by 16.7%.