Using cluster-based stereotyping to foster human-robot cooperation

Abstract

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.