Real-Time Emotion Recognition in Human-Robot Interaction Using Deep AI Models

Abstract

Emotion recognition in human-robot interaction (HRI) is important in order to develop socially aware and responsive robotic systems. In this work, a near real-time emotion recognition architecture is introduced which combines deep AI models such as CNN, and recurrent architectures (LSTM/ GRU) for audio and visual-based emotion detection. The system is tested on the benchmark datasets like FER-2013 and RAVDESS. The goal is to provide a strong and scalable approach that can serve as a step toward the full integration of emotionally intelligent robots into everyday life, to encourage empathic, adaptive, and rewarding human-robot interaction. The proposed deep AI model was tested on a dataset comprising 5,000 multimodal samples of human emotional expressions collected in controlled and real-world human-robot interaction scenarios. The dataset included five emotional categories: Happy, Sad, Angry, Neutral, and Surprise. Experimental results show the effectiveness of the proposed system based on the public datasets, and its practical use in the simulated human-robot interaction (HRI) scenario. Also, the proposed approach provides high accuracy and low inference delay, which can support robotic agents to have effective emotion-adaptive behaviors in live-interaction environments.