NeuroRobo: Bridging the Emotional Gap in Human-Robot Interaction with Facial Sentiment Analysis, Object Detection, and Behavior Prediction

Efficient and personalized human-robot interaction is a critical goal in robotics research. In this study, we propose a novel approach to enhance human-robot interaction by integrating facial sentiment analysis, object detection, and behavior prediction into a bot powered by Blender face technology. Our proposed system enables the bot to perceive and respond to the emotional states and preferences of individuals, creating a more intuitive and engaging interaction experience. By integrating lip syncing capabilities and object recognition functionality through webcam integration, the proposed solution seeks to enhance the authenticity and intuitiveness of user experiences. Through the utilization of Blender animation tools and Natural Language Processing methods, our solution facilitates seamless interaction between humans and neuro robots, contributing to improved outcomes and well-being.