Much worldly semantic knowledge can be encoded in large language models (LLMs). Such information could be of great use to robots that want to carry out high-level, temporally extended commands stated in natural language. However, the lack of real-world experience that language models have is a key limitation that makes it challenging to use them for decision-making inside a particular embodiment. This research assesses the feasibility of using LLM (GPT-3.5-turbo chatbot by OpenAI) for robotic path planning. The shortcomings of conventional approaches to managing complex environments and developing trustworthy plans for shifting environmental conditions serve as the driving force behind the research. Due to the sophisticated natural language processing abilities of LLM, the capacity to provide effective and adaptive path-planning algorithms in real-time, great accuracy, and few-shot learning capabilities, GPT-3.5-turbo is well suited for path planning in robotics. In numerous simulated scenarios, the research compares the performance of GPT-3.5-turbo with that of state-of-the-art path planners like Rapidly Exploring Random Tree (RRT) and A*. We observed that GPT-3.5-turbo is able to provide real-time path planning feedback to the robot and outperforms its counterparts. This paper establishes the foundation for LLM-powered path planning for robotic systems.

翻译：大量世界语义知识可编码于大语言模型（LLM）中。此类信息对于希望执行自然语言描述的高层次、长时间跨度指令的机器人极具价值。然而，语言模型缺乏真实世界经验的关键局限性，使其难以直接用于特定具身系统的决策。本研究评估了使用LLM（OpenAI的GPT-3.5-turbo聊天机器人）进行机器人路径规划的可行性。传统方法在应对复杂环境及为动态环境条件制定可靠规划方面的缺陷，构成了本研究的主要驱动力。得益于LLM强大的自然语言处理能力、实时提供高效自适应路径规划算法的能力、高精度特性及少样本学习能力，GPT-3.5-turbo非常适合机器人路径规划任务。通过多项仿真场景实验，本研究将GPT-3.5-turbo与快速探索随机树（RRT）及A*等先进路径规划器的性能进行对比。实验结果表明，GPT-3.5-turbo能够为机器人提供实时路径规划反馈，且性能优于对比算法。本文为基于LLM的机器人系统路径规划奠定了研究基础。