用可组合思维图提升车-机协同系统推理能力
CGoT: A Novel Inference Mechanism for Embodied Multi-Agent Systems Using Composable Graphs of Thoughts
- 提出CGOT机制，让车辆携带机器人实现任务协作
- 实验验证该机制显著提升多智能体系统运行效率
- 适合研究自动驾驶与服务机器人协同的学者
自驾车与服务机器人在工业应用和日常生活中日益融合。本文提出一种新型车-机协同系统，由两辆自主车辆将服务机器人运送至园区内指定位置并执行任务。研究探索将大语言模型（LLMs）引入该系统以增强运行效率，并最大化车辆与机器人之间的协作潜力。为此，本文提出一种名为CGoT（Composable Graphs of Thoughts）的新推理机制，适用于代理携带另一代理的场景。实验结果验证了该方法的有效性。
原文摘要 · Abstract (English)
The integration of self-driving cars and service robots is becoming increasingly prevalent across a wide array of fields, playing a crucial and expanding role in both industrial applications and everyday life. In parallel, the rapid advancements in Large Language Models (LLMs) have garnered substantial attention and interest within the research community. This paper introduces a novel vehicle-robot system that leverages the strengths of both autonomous vehicles and service robots. In our proposed system, two autonomous ego-vehicles transports service robots to locations within an office park, where they perform a series of tasks. The study explores the feasibility and potential benefits of incorporating LLMs into this system, with the aim of enhancing operational efficiency and maximizing the potential of the cooperative mechanisms between the vehicles and the robots. This paper proposes a novel inference mechanism which is called CGOT toward this type of system where an agent can carry another agent. Experimental results are presented to validate the performance of the proposed method.
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