  We propose, analyze, and experimentally verify a new proactive approach for
robot social navigation driven by the robot's "opinion" for which way and by
how much to pass human movers crossing its path. The robot forms an opinion
over time according to nonlinear dynamics that depend on the robot's
observations of human movers and its level of attention to these social cues.
For these dynamics, it is guaranteed that when the robot's attention is greater
than a critical value, deadlock in decision making is broken, and the robot
rapidly forms a strong opinion, passing each human mover even if the robot has
no bias nor evidence for which way to pass. We enable proactive rapid and
reliable social navigation by having the robot grow its attention across the
critical value when a human mover approaches. With human-robot experiments we
demonstrate the flexibility of our approach and validate our analytical results
on deadlock-breaking. We also show that a single design parameter can tune the
trade-off between efficiency and reliability in human-robot passing. The new
approach has the additional advantage that it does not rely on a predictive
model of human behavior.
