用大模型构建多智能体系统，自动完成机器人任务分析到路径生成
Multi-Agent Systems for Robotic Autonomy with LLMs
- 三智能体协同：任务分析、机械设计、强化学习策略生成
- 支持代码与技术报告输出，可生成可执行的机器人设计方案
- 在GPT和DeepSeek模型上验证，适配科研与工业场景
自大型语言模型（LLMs）问世以来，基于此类模型的研究持续获得学术界关注与影响，尤其在人工智能与机器人领域。本文提出一种基于大模型的多智能体框架，用于构建集成的机器人任务分析、机械设计与路径生成系统。该框架包含三个核心智能体：任务分析师、机器人设计师与强化学习设计师。输出以多模态形式呈现，如代码文件或技术报告，增强可理解性与实用性。为评估通用性，我们使用GPT与DeepSeek系列模型进行对比实验。结果表明，在提供适当任务输入时，所提系统能够设计出可行的机器人及其控制策略，展现出显著提升科研与工业应用中机器人系统开发效率与可及性的潜力。
原文摘要 · Abstract (English)
Since the advent of Large Language Models (LLMs), various research based on such models have maintained significant academic attention and impact, especially in AI and robotics. In this paper, we propose a multi-agent framework with LLMs to construct an integrated system for robotic task analysis, mechanical design, and path generation. The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer. Outputs are formatted as multimodal results, such as code files or technical reports, for stronger understandability and usability. To evaluate generalizability comparatively, we conducted experiments with models from both GPT and DeepSeek. Results demonstrate that the proposed system can design feasible robots with control strategies when appropriate task inputs are provided, exhibiting substantial potential for enhancing the efficiency and accessibility of robotic system development in research and industrial applications.
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。