The increasing application of industrial robots in modern production systems contrasts with a persistently high programming complexity that requires specialized know-how and creates substantial entry barriers. This work addresses this problem by introducing a systematic approach to robot programming based on Large Language Models (LLMs) that automatically translates natural language task descriptions into executable robot programs. The solution follows a two-stage pipeline: in Stage 1, the LLM structures the input into coherent process steps, and in Stage 2 these process steps are transformed into C++ code using a high-level function library. The performance is evaluated in simulation for the automated electrical cabinet assembly use case with terminal blocks, which is a significant element of various production processes. The architecture, based on the Robot Operating System 2 (ROS2) and MoveIt2, further integrates a standardized AutomationML-based configuration management for dynamic parameter handling and persistent state storage. A graphical user interface visualizes intermediate results, enables manual interventions and enables a simple operation for potential users without programming experience. The evaluation of the presented approach shows a success rate of up to 95 % for interpreting natural language instructions and generating code in the application scenario focused. The system reliably recognizes object attributes and correctly executes complex assembly instructions. In general, this work demonstrates how modern LLMs can bridge the semantic gap between human intent and robotic code for industrial applications. The developed high-level abstraction makes the system usable for non-programmers, highlights the potential for intuitive robot programming, and simultaneously identifies concrete technical challenges.
Syniawa et al. (2026) studied this question.