Using cluster-based stereotyping to foster human-robot cooperation

Psychologists note that humans regularly use categories to simplify and speed up the process of person perception [1]. The influence of categorical thinking on interpersonal expectations is commonly referred to as a stereotype. The ability to bootstrap the process of learning about a newly encountered, unknown person is critical for robots interacting in complex and dynamic social situations. This article contributes a novel cluster-based algorithm that allows a robot to create generalized models of its interactive partner. These generalized models, or stereotypes, act as a source of information for predicting the human´s behavior and preferences. We show, in simulation and using real robots, that these stereotyped models of the partner can be used to bootstrap the robot´s learning about the partner in spite of significant error. The results of this work have potential implications for social robotics, autonomous agents, and possibly psychology.