The Emotionally Intelligent Robot: Improving Socially-aware Human Prediction in Crowded Environments

Aniket Bera, Tanmay Randhavane, Dinesh Manocha; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


We present a real-time emotion-aware navigation algorithm for social robots. 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 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.

Related Material


[pdf]
[bibtex]
@InProceedings{Bera_2019_CVPR_Workshops,
author = {Bera, Aniket and Randhavane, Tanmay and Manocha, Dinesh},
title = {The Emotionally Intelligent Robot: Improving Socially-aware Human Prediction in Crowded Environments},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}