Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces

• URL: http://arxiv.org/abs/2505.03295v1
• Date: Tue, 06 May 2025 08:27:04 GMT
• Title: Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces
• Authors: Luis Miguel Vieira da Silva, Aljosha Köcher, Nicolas König, Felix Gehlhoff, Alexander Fay,
• Abstract summary: We present a method that treats capabilities as contracts for skill implementations and leverages large language models to generate code based on natural language user input.<n>A key feature of our approach is the integration of existing software libraries and interface technologies.<n>We introduce a framework that allows users to incorporate their own libraries and resource interfaces into the code generation process.
• Score: 40.638726615548954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern automation systems increasingly rely on modular architectures, with capabilities and skills as one solution approach. Capabilities define the functions of resources in a machine-readable form and skills provide the concrete implementations that realize those capabilities. However, the development of a skill implementation conforming to a corresponding capability remains a time-consuming and challenging task. In this paper, we present a method that treats capabilities as contracts for skill implementations and leverages large language models to generate executable code based on natural language user input. A key feature of our approach is the integration of existing software libraries and interface technologies, enabling the generation of skill implementations across different target languages. We introduce a framework that allows users to incorporate their own libraries and resource interfaces into the code generation process through a retrieval-augmented generation architecture. The proposed method is evaluated using an autonomous mobile robot controlled via Python and ROS 2, demonstrating the feasibility and flexibility of the approach.

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