Abstract
We present a real-time, data-driven algorithm to
enhance the social-invisibility of autonomous vehicles 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 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.
Paper
Socially Invisible Navigation for Intelligent Vehicles, IROS 2018 (workshop).
Aniket Bera, Tanmay Randhavane, Emily Kubin, Austin Wang, Kurt Gray, and Dinesh Manocha
@article{Bera2018TheSI,
title={The Socially Invisible Robot Navigation in the Social World Using Robot Entitativity},
author={Aniket Bera and Tanmay Randhavane and Emily Kubin and Austin Wang and Kurt Gray and Dinesh Manocha},
journal={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2018},
pages={4468-4475}
}