The Socially Invisible Robot: Navigation in the Social World using Robot Entitativity
This addresses the challenge of integrating robots into human social environments without disruption, though it is incremental as it builds on prior psychological research.
The paper tackles the problem of robots being noticed and causing negative reactions in crowds by developing a real-time algorithm that minimizes robot entitativity, resulting in socially invisible multi-robot systems that can avoid and influence pedestrians without eliciting strong emotional reactions.
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.