Abstract
Autonomous navigation is usually trained offline in diverse scenarios and fine-tuned online subject to real-world experiences. However, the real world is dynamic and changeable, and many environmental encounters/effects are not accounted for in real-time due to difficulties in describing them within offline training data or hard to describe even in online scenarios. However, we know that the human operator can describe these dynamic environmental encounters through natural language, adding semantic context. The research is to deploy Large Language Models (LLMs) to perform real-time contextual code adjustment to autonomous navigation. The challenge not evaluated in literature is what LLMs are appropriate and where should these computationally heavy algorithms sit in the computation-communication edge-cloud computing architectures. In this paper, we evaluate how different LLMs can adjust both the navigation map parameters dynamically (e.g., contour map shaping) and also derive navigation task instruction sets. We then evaluate which LLMs are most suitable and where they should sit in future edge-cloud of 6G telecommunication architectures.
Abstract (translated)
自主导航通常在多样化的场景中离线训练，然后根据实际经验在线微调。然而，现实世界是动态且多变的，许多环境交互或影响由于难以描述而无法实时纳入离线训练数据中，甚至在在线场景下也难以准确描述。但是我们知道，人类操作员可以通过自然语言来描述这些动态的环境互动，并添加语义上下文信息。研究旨在部署大型语言模型（LLMs）以进行实时上下文代码调整，应用于自主导航系统。目前文献未评估的问题是：哪些LLM模型适合这一任务以及这些计算密集型算法应位于边缘-云计算架构中的哪个位置。 本文将评估不同的LLM如何动态调整导航地图参数（如地形图的形状），并生成导航任务指令集。然后我们将评估哪种LLM最适合这项工作，并确定它们在未来6G电信边缘-云架构中应该占据的位置。
URL
https://arxiv.org/abs/2504.00607
