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

The Emotionally Intelligent Robot: Improving Social Navigation in Crowded Environments