Improvement of object reference recognition through human robot alignment

This paper reports an interactive approach to improve the recognition performance by robots of objects indicated by humans during human-robot interaction. We developed an approach based on two findings in conversations where a human refers to an object, which is confirmed by a robot. First, humans tend to use the same words or gestures as the robot in a phenomenon called alignment. Second, humans tend to decrease the amount of information in their references when the robot uses excess information in its confirmations: in other words, alignment inhibition. These findings lead to the following design; a robot should use enough information without being excessive to identify objects to improve recognition accuracy because humans will eventually use similar information to refer to those objects by alignment. If humans more frequently use the same information to identify objects, the robot can more easily recognize those being indicated by humans. To verify our design, we developed a robotic system to recognize the objects to which humans referred and conducted a control experiment that had 2 × 3 conditions; one factor was the robot's confirmation way and another was the arrangement of the objects. The first factor had two levels to identify objects: enough information and excess information. The second factor had three levels: congestion, two groups, and a sparse set. We measured the recognition accuracy of the objects humans referred to and the amount of information in their references. The success rate of the recognition and information amount was higher in the adequate information condition than in the excess condition in a particular situation. The results suggested the possibility that our proposed interactive approach improved recognition performance.