Forskningsbibliotek / 2026 / Konferensbidrag · preprint
Retrieval-grounded robot program generation and simulation-based correction via Model Context Protocol
arXiv:2608.21417 · Accepted for CIE53 2026 · 2026
Accepterat till CIE53 2026 och presenterat i Abu Dhabi. Den länkade versionen är en arXiv-preprint.
Abstract
Engelskt originalabstract
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.
Originalkälla: arXiv ↗
I korthet
Arbetsflödet kopplar RAG-baserad generering av ABB RAPID till RobotStudio via MCP. Simuleringsåterkoppling avslöjar exekveringsfel som statisk kodkontroll missar och stödjer iterativ korrigering.
Huvudpunkter
- Förankrar kodgenerering i verifierad dokumentation och produktionsmallar med två RAG-flöden.
- Använder en MCP-server för kodöverföring, simulering och diagnostik i ABB RobotStudio.
- Identifierar fel som statisk kontroll missar; expertkonfiguration och slutlig granskning behövs fortfarande.
Citera arbetet
Zhichao Zhou, Siyuan Chen, Omkar Salunkhe, Ebru Turanoglu Bekar, Johan Stahre, Anders Skoogh (2026). Retrieval-grounded robot program generation and simulation-based correction via Model Context Protocol. arXiv:2608.21417 · Accepted for CIE53 2026. https://doi.org/10.48550/arXiv.2608.21417
@misc{Chen2026RobotProgramGeneration,
title = {{Retrieval-grounded robot program generation and simulation-based correction via Model Context Protocol}},
author = {Zhou, Zhichao and Chen, Siyuan and Salunkhe, Omkar and Turanoglu Bekar, Ebru and Stahre, Johan and Skoogh, Anders},
year = {2026},
doi = {10.48550/arXiv.2608.21417},
url = {https://arxiv.org/abs/2608.21417},
eprint = {2608.21417},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
note = {arXiv preprint; accepted for CIE53 2026; presented in Abu Dhabi}
}
Originalabstractet återges från den länkade källposten. Översikten och huvudpunkterna ger korta förklaringar av studien.