用大模型自动生成可动态适应的机器人规划网络
LLM-mediated Dynamic Plan Generation with a Multi-Agent Approach
- 基于GPT-4o根据环境状态自动构建可互联的智能体网络
- 自动生成网络在全面性和通用性上优于人工设计
- 适合研发具身智能、自动驾驶等复杂系统
高适应性动态环境规划方法对自主与多功能机器人的发展至关重要。本文提出一种利用大语言模型（GPT-4o）自动生成可适应动态环境的智能体网络的方法。该方法采集环境‘状态’（包括条件与目标），并据此生成智能体，这些智能体基于特定条件相互连接，形成兼具灵活性与通用性的网络结构。通过对比实验评估自动生成网络与人工构建网络的性能，验证了所提方法生成网络的全面性及其更高的通用性。该研究为机器人、自动驾驶车辆、智能系统等复杂环境中的通用规划方法发展提供了重要进展。
原文摘要 · Abstract (English)
Planning methods with high adaptability to dynamic environments are crucial for the development of autonomous and versatile robots. We propose a method for leveraging a large language model (GPT-4o) to automatically generate networks capable of adapting to dynamic environments. The proposed method collects environmental "status," representing conditions and goals, and uses them to generate agents. These agents are interconnected on the basis of specific conditions, resulting in networks that combine flexibility and generality. We conducted evaluation experiments to compare the networks automatically generated with the proposed method with manually constructed ones, confirming the comprehensiveness of the proposed method's networks and their higher generality. This research marks a significant advancement toward the development of versatile planning methods applicable to robotics, autonomous vehicles, smart systems, and other complex environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。