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

SocioSense: Robot Navigation Amongst Pedestrians with Social and Psychological Constraints