Human-Robot Interaction Interface Design Using Computer Vision And Natural Language Processing

Abstract

This paper presents an innovative approach to human-robot interaction (HRI) interface design that integrates computer vision and natural language processing (NLP) technologies. The proposed system enables intuitive communication between humans and robots through multimodal interaction, combining visual gesture recognition, facial expression analysis, and voice command processing. Our methodology employs deep learning architectures including convolutional neural networks (CNNs) for visual processing and transformer models for language understanding. Experimental results demonstrate 94.2% accuracy in gesture recognition, 91.8% accuracy in emotion detection, and 96.3% accuracy in natural language command interpretation. The system achieves real-time performance with an average response time of 185ms, making it suitable for practical robotic applications. This research contributes to the advancement of intuitive HRI systems that can adapt to natural human communication patterns.