用轻量AI团队让建筑机器人零样本自适应规划任务，性价比超GPT-4o十倍
Zero-shot adaptable task planning for autonomous construction robots: a comparative study of lightweight single and multi-AI agent systems
- 设计单/多AI代理系统，用轻量LLM/VLM实现任务规划
- 四代理团队在三项施工任务中性能超GPT-4o，成本低十倍
- 三至四代理团队展现更强泛化能力，适合复杂动态环境
未来建筑行业将依赖机器人，但面临成本高与任务适应难的问题。本研究探索基础模型提升建筑机器人任务规划的适应性与泛化能力。提出四个基于轻量开源大语言模型（LLMs）和视觉语言模型（VLMs）的模型，包括一个单代理和三个多代理协作系统，用于生成机器人动作规划。在涂装工、安全检查员、地砖铺设三个角色上评估。结果表明，四代理团队在多数指标上优于当前最先进的GPT-4o，且成本低十倍；三至四代理团队展现出更优的泛化能力。通过分析代理行为对输出的影响，深化了对AI协作机制的理解，为非结构化环境中未来研究提供支持。
原文摘要 · Abstract (English)
Robots are expected to play a major role in the future construction industry but face challenges due to high costs and difficulty adapting to dynamic tasks. This study explores the potential of foundation models to enhance the adaptability and generalizability of task planning in construction robots. Four models are proposed and implemented using lightweight, open-source large language models (LLMs) and vision language models (VLMs). These models include one single agent and three multi-agent teams that collaborate to create robot action plans. The models are evaluated across three construction roles: Painter, Safety Inspector, and Floor Tiling. Results show that the four-agent team outperforms the state-of-the-art GPT-4o in most metrics while being ten times more cost-effective. Additionally, teams with three and four agents demonstrate the improved generalizability. By discussing how agent behaviors influence outputs, this study enhances the understanding of AI teams and supports future research in diverse unstructured environments beyond construction.
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