基于检索的机器人程序生成与基于仿真的修正：通过模型上下文协议
Retrieval-grounded robot program generation and simulation-based correction via Model Context Protocol
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中文总结 AI 辅助
该研究针对柔性制造中机器人快速重编程需求，提出结合RAG与MCP的工作流，可生成并修正ABB RAPID机器人程序，借助仿真暴露代码执行故障，减少专家监督需求。
中文摘要 AI 辅助
柔性制造要求工业机器人能随产品变体变化快速重新编程。本文提出一种基于语言模型的工作流，可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。双路检索增强生成（RAG）管道将代码生成锚定在经过验证的技术文档和生产模板上，减少未锚定语言模型产生的领域特定错误。自定义模型上下文协议（MCP）服务器将语言模型客户端直接连接到ABB RobotStudio，实现代码自动上传、仿真执行与诊断反馈。评估结合了30个查询的检索基准、限定范围的代码生成检查，以及模拟拾取-放置制造单元中的RobotStudio案例研究。仿真循环可暴露静态和语义检查无法捕获的执行故障，包括吸盘释放高度错误、无法到达的放置目标，以及依赖配置的恢复运动。结果表明，RAG与MCP可将锚定代码生成与工业机器人仿真软件的可执行反馈相连，同时减少但未消除专家设置与最终监督需求。
英文摘要
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.
发表机构
- Chalmers University of Technology（查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。
