Robots That Stereotype: Creating and Using Categories of People for Human-Robot Interaction
Psychologists note that humans use categories to simplify and speed up the process of person perception. Applications that require a robot to interact with a variety of different people will demand the creation of stereotypes representing different categories of people. This article presents a method for stereotype learning and usage by a robot. Both robot and simulation experiments are used to examine the benefits and challenges associated with stereotype usage. Our results indicate that stereotypes can serve as a means of feature selection and for inferring a person’s appearance from observations of their actions. The results also show that the timing of certain types of errors impact the stereotype creation. This article concludes by describing the limitations, ethical ramifications, and potential applications of this research.
Human-robot interaction, stereotype, user model, recognition, concept learning
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