A Rubber-sheet Transformation Model for Personalized Human-Robot Proxemics

Abstract

The deployment of autonomous robots in human environments requires an understanding of social interactions and the factors that influence them. Human-robot proxemics is an important factor that impacts interactions, and modeling personalized proxemic behavior has always been a challenge, as it depends on multiple user attributes, including gender, age, and height. In this paper, we propose a novel approach that uses rubber-sheet transformation models to represent human-robot proxemics. We do this by collecting human-robot interpersonal distance data from 20 users and model it with respect to their height, age, gender, and the angle at which the robot approaches. We present an evaluation of the model, and the outcome of our results, which show a promising approximation of proxemic distances based on different user attributes. Finally, we provide a coefficient table for rubber-sheet models to lay the foundation for personalized human-robot proxemics and outline future research directions.