The Socially Invisible Robot: Navigation in the Social World using Robot Entitativity

TL;DR

This paper introduces a real-time, data-driven algorithm that reduces the social visibility of robots in crowds by controlling their entitativity, thereby minimizing negative reactions and enabling socially-invisible navigation.

Contribution

It presents a novel entitativity-based navigation algorithm grounded in psychological research, with a user study linking robot behavior to emotional responses and demonstrating practical applications.

Findings

Lower entitativity reduces negative emotional reactions.

The algorithm effectively minimizes social visibility in various environments.

Robots can influence pedestrian behavior without causing discomfort.

Abstract

We present a real-time, data-driven algorithm to enhance the social-invisibility of robots within crowds. Our approach is based on prior psychological research, which reveals that people notice and--importantly--react negatively to groups of social actors when they have high entitativity, moving in a tight group with similar appearances and trajectories. In order to evaluate that behavior, we performed a user study to develop navigational algorithms that minimize entitativity. This study establishes a mapping between emotional reactions and multi-robot trajectories and appearances and further generalizes the finding across various environmental conditions. We demonstrate the applicability of our entitativity modeling for trajectory computation for active surveillance and dynamic intervention in simulated robot-human interaction scenarios. Our approach empirically shows that various levels of entitative robots can be used to both avoid and influence pedestrians while not eliciting strong emotional reactions, giving multi-robot systems socially-invisibility.