Adaptive Generalized Social Space Delimitation for Human-Robot Interaction Tasks

Abstract

Human-Robot Interaction (HRI) research is of paramount importance as it addresses the rapidly evolving landscape where robots are increasingly integrated into everyday life. Social spaces refer to the invisible zones that individuals maintain around themselves, dictating appropriate distances for various types of interactions. This concept is pivotal in HRI, as it ensures that robots engage with humans in ways that are both comfortable and respectful of personal boundaries. In this paper, we propose a novel social space delimitation approach based on deformable elastic bands. Our method leverages the flexibility and adaptability of elastic band theory to dynamically define and adjust social spaces around individuals and groups. The results, based on both static and dynamic cases as well as a real-world dataset, show that our approach achieves a responsive and accurate representation of social spaces, that can accommodate varying group configurations and environmental conditions.