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

Abstract

Vast reservoirs of semantic knowledge are encap-sulated within large language models (LLMs), proffering sub-stantial 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 compli-cating 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 formu-lating 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.