Despite their powerful semantic understanding and code generation capabilities, Large Language Models (LLMs) still face challenges when dealing with complex tasks. Multi agent strategy generation and motion control are highly complex domains that inherently require experts from multiple fields to collaborate. To enhance multi agent strategy generation and motion control, we propose an innovative architecture that employs the concept of a cloud edge end hierarchical structure. By leveraging multiple large language models with distinct areas of expertise, we can efficiently generate strategies and perform task decomposition. Introducing the cosine similarity approach,aligning task decomposition instructions with robot task sequences at the vector level, we can identify subtasks with incomplete task decomposition and iterate on them multiple times to ultimately generate executable machine task sequences.The robot is guided through these task sequences to complete tasks of higher complexity. With this architecture, we implement the process of natural language control of robots to perform complex tasks, and successfully address the challenge of multi agent execution of open tasks in open scenarios and the problem of task decomposition.

翻译：尽管大型语言模型（LLMs）具备强大的语义理解与代码生成能力，但处理复杂任务时仍面临挑战。多智能体策略生成与运动控制作为高度复杂的领域，天然需要多领域专家协同工作。为增强多智能体策略生成与运动控制能力，本文提出一种创新架构，采用云边端层级化结构概念。通过利用多个具有不同专业领域知识的大型语言模型，可高效生成策略并执行任务分解。引入余弦相似度方法，将任务分解指令与机器人任务序列在向量层面进行对齐，能够识别存在不完整分解的子任务并进行多轮迭代优化，最终生成可执行的机器任务序列。机器人通过执行这些任务序列完成更高复杂度的任务。基于该架构，我们实现了自然语言控制机器人执行复杂任务的流程，并成功解决了开放场景下多智能体执行开放任务以及任务分解的挑战。