  We present a real-time algorithm, SocioSense, for socially-aware navigation
of a robot amongst pedestrians. Our approach computes time-varying behaviors of
each pedestrian using Bayesian learning and Personality Trait theory. These
psychological characteristics are used for long-term path prediction and
generating proximic characteristics for each pedestrian. We combine these
psychological constraints with social constraints to perform human-aware robot
navigation in low- to medium-density crowds. The estimation of time-varying
behaviors and pedestrian personalities can improve the performance of long-term
path prediction by 21%, as compared to prior interactive path prediction
algorithms. We also demonstrate the benefits of our socially-aware navigation
in simulated environments with tens of pedestrians.
