The Emotionally Intelligent Robot: Improving Social Navigation in Crowded Environments
This work addresses the challenge of making robots more socially aware and safe in human-populated spaces, though it is incremental by building on existing emotion models and navigation techniques.
The paper tackles the problem of social navigation for robots in crowded environments by incorporating real-time emotion detection from pedestrian faces and trajectories, achieving an emotion detection accuracy of 85.33% and demonstrating improved navigation in simulated and real-world settings.
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.