  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.
