Comparative Analysis of GPT-4 and LLaMA 3.2 Integration With Speech Processing Models for Enhancing Human–Robot Interaction and Motion Control in Real-World Applications

Human-Robot Interaction (HRI) in robots finds wide applications today such as personal assistant robots, autonomous vehicles, healthcare support robots, industrial robots and many more, which receive interpreted commands to perform functions ranging from home automation to real-time assembly line operations. This paper provides a comparative study of different Natural Language Processing (NLP) models that are created by combining advanced Large Language Models (LLMs) with speech processing technologies to create a more intuitive, adaptable, and accurate system for robots. By assessing performance metrics including accuracy, response time and robustness, this paper identifies the best generalized model for the real-world application in robotics. In fact, this framework enables natural language understanding and speech generation to be combined effectively, which can help robots respond quickly to spoken requests, even in dynamic environments.