The Emotionally Intelligent Robot: Improving Social Navigation in Crowded Environments

Abstract

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.

References in corpus (5)

• From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots
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• Uncertainty-Aware Reinforcement Learning for Collision Avoidance
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Cited by in corpus (2)

• FVA: Modeling Perceived Friendliness of Virtual Agents Using Movement Characteristics
• ROSbag-based Multimodal Affective Dataset for Emotional and Cognitive States