用自然语言生成机器人程序，自动仿真纠错，提升制造灵活性。
Retrieval-grounded robot program generation and simulation-based correction via Model Context Protocol
- 通过双流检索增强生成，结合技术文档与模板生成可靠代码。
- 仿真循环发现静态检查无法捕捉的执行错误，如吸盘释放高度异常。
- 适配工业机器人开发人员，尤其适合需要快速重构产线的场景。
柔性制造要求工业机器人随产品变型快速重编程。本文提出基于语言模型的工作流，从自然语言任务描述生成、验证并迭代修正ABB RAPID机器人程序。采用双流检索增强生成（RAG）管道，将代码生成锚定于经验证的技术文档与生产模板，降低非上下文语言模型产生的领域错误。定制的Model Context Protocol（MCP）服务器实现语言模型客户端与ABB RobotStudio的直接连接，支持自动上传代码、执行仿真及获取诊断反馈。评估包含30个查询的检索基准、限定范围的代码生成测试以及在模拟抓取-放置制造单元中的RobotStudio案例研究。仿真循环揭示了仅靠静态和语义检查无法发现的执行失败，包括吸盘释放高度错误、不可达放置目标及依赖配置的恢复动作。结果表明，RAG与MCP可将受控代码生成与工业机器人仿真软件的可执行反馈相衔接，但仍需专家参与设置与最终审核。
原文摘要 · Abstract (English)
Flexible manufacturing requires industrial robots to be reprogrammed rapidly as product variants change. This paper presents a language-model-based workflow that generates, validates, and iteratively corrects ABB RAPID robot programs from natural language task descriptions. A dual-stream retrieval-augmented generation (RAG) pipeline grounds code generation in verified technical documentation and production templates, reducing domain-specific errors produced by ungrounded language models. A custom Model Context Protocol (MCP) server connects the language-model client directly to ABB RobotStudio for automated code upload, simulation execution, and diagnostic feedback. The evaluation combines a 30-query retrieval benchmark, scoped code-generation checks, and RobotStudio case studies in a simulated pickand- place manufacturing cell. The simulation loop exposes execution failures that static and semantic checks alone cannot catch, including suction release-height errors, unreachable placement targets, and configuration-dependent recovery motions. The results show how RAG and MCP can connect grounded code generation with executable feedback from industrial robot simulation software, while reducing but not eliminating expert setup and final supervision.
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