Precision Kinematic Path Optimization for High-DoF Robotic Manipulators Utilizing Advanced Natural Language Processing Models

Ye Zhang, Kangtong Mo, Wenyan Liu, Fangzhou Shen, Xuanzhen Xu, Letian Xu, Xiran Su

Abstract: Vast reservoirs of semantic knowledge are encapsulated within large language models (LLMs), proffering substantial utility to robotic systems tasked with executing intricate, temporally prolonged commands articulated in natural language. Nonetheless, the inherent deficit of real-world experiential data within LLMs constitutes a formidable limitation, thereby complicating their deployment in decision-making processes pertinent to specific embodiments. This study delves into the viability of utilizing an LLM, particularly OpenAI’s GPT-4o, for high-DoF robotic manipulator trajectory planning. The impetus for this investigation stems from the shortcomings of conventional methodologies in navigating complex environments and formulating robust plans under dynamic conditions. By harnessing the sophisticated natural language processing prowess of LLMs, GPT-4o demonstrates potential in furnishing efficacious and adaptive path-planning algorithms in real-time, characterized by high precision and adeptness in few-shot learning. Through an array of simulated scenarios, this research contrasts the performance of GPT-4o with state-of-the-art path planners, including Rapidly Exploring Random Tree (D* lite) and A*. Our empirical findings suggest that GPT-4o can provide real-time path-planning feedback to robots, exceeding the performance metrics of its conventional counterparts. This paper establishes a foundational framework for the integration of LLMs in robotic path planning, underscoring the transformative potential of LLM-empowered robotic systems.