Multi-robot coordination has traditionally relied on a mission-specific and expert-driven pipeline, where natural language mission descriptions are manually translated by domain experts into mathematical formulation, algorithm design, and executable code. This conventional process is labor-intensive, inaccessible to non-experts, and inflexible to changes in mission requirements. Here, we propose LAN2CB (Language to Collective Behavior), a novel framework that leverages large language models (LLMs) to streamline and generalize the multi-robot coordination pipeline. LAN2CB transforms natural language (NL) mission descriptions into executable Python code for multi-robot systems through two core modules: (1) Mission Analysis, which parses mission descriptions into behavior trees, and (2) Code Generation, which leverages the behavior tree and a structured knowledge base to generate robot control code. We further introduce a dataset of natural language mission descriptions to support development and benchmarking. Experiments in both simulation and real-world environments demonstrate that LAN2CB enables robust and flexible multi-robot coordination from natural language, significantly reducing manual engineering effort and supporting broad generalization across diverse mission types. Website: https://sites.google.com/view/lan-cb

翻译：传统多机器人协调通常依赖于任务特定且专家驱动的流程，其中自然语言任务描述需由领域专家手动转化为数学建模、算法设计和可执行代码。这一传统过程不仅劳动密集、对非专家用户极不友好，且难以适应任务需求的动态变化。本文提出LAN2CB（语言到群体行为）框架，该创新系统利用大型语言模型实现多机器人协调流程的标准化与泛化。LAN2CB通过两个核心模块将自然语言任务描述转化为多机器人系统的可执行Python代码：（1）任务分析模块——将任务描述解析为行为树；（2）代码生成模块——基于行为树与结构化知识库生成机器人控制代码。我们进一步构建了自然语言任务描述数据集以支持系统开发与性能评估。仿真环境与真实场景的实验表明，LAN2CB能够通过自然语言实现鲁棒且灵活的多机器人协调，显著降低人工工程成本，并支持跨多种任务类型的广泛泛化能力。项目网站：https://sites.google.com/view/lan-cb