Developing RAGs for robot code generation

Abstract

Abstract The emergence of generative AI marks a transformative shift in industrial automation. Traditional robot programming relies on manually written, low-level code that requires specialised expertise, limiting flexibility and accessibility. Recent advances in Large Language Models (LLMs) such as ChatGPT and Mistral introduce new paradigms for automated code generation. However, concerns about data security, model hallucinations, and the opaque reasoning of generative systems continue to hinder their adoption in industry. A promising approach to address these challenges is Retrieval-Augmented Generation (RAG), where the generative model draws on curated, domain-specific data sources controlled by the user. By combining structured knowledge retrieval with generative inference, RAG-based systems can produce robot code that is not only more accurate and context-aware but also verifiable and transparent. This approach enhances user trust and enables safer integration of AI in industrial settings. This paper explores the application of Retrieval-Augmented Generation (RAG)-based architectures - a method that combines information retrieval with LLMs - for robot code generation. RAG-based systems enable LLMs to access and utilise domain-specific data, thereby grounding their outputs in reliable knowledge. By leveraging these techniques, robotics developers can achieve more accurate and efficient code generation, potentially accelerating innovation in autonomous systems. Furthermore, it presents a conceptual framework for RAG-enhanced robot programming that balances autonomy with human oversight. The proposed framework enhances the adaptability and intelligence of automated programming by providing a transparent, controllable, and explainable alternative to conventional AI-driven methods, paving the way for more reliable and human-centric automation in future manufacturing environments.

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