  We present a real-time algorithm for emotion-aware navigation of a robot
among pedestrians. Our approach estimates time-varying emotional behaviors of
pedestrians from their faces and trajectories using a combination of
Bayesian-inference, CNN-based learning, and the PAD
(Pleasure-Arousal-Dominance) model from psychology. These PAD characteristics
are used for long-term path prediction and generating proxemic constraints for
each pedestrian. We use a multi-channel model to classify pedestrian
characteristics into four emotion categories (happy, sad, angry, neutral). In
our validation results, we observe an emotion detection accuracy of 85.33%. We
formulate emotion-based proxemic constraints to perform socially-aware robot
navigation in low- to medium-density environments. We demonstrate the benefits
of our algorithm in simulated environments with tens of pedestrians as well as
in a real-world setting with Pepper, a social humanoid robot.
