Wrede, KonstantinKonstantinWredeJain, VineetaVineetaJainDehmel, MartinMartinDehmelHartmann, NickNickHartmannLange, RobertRobertLange2025-05-122025-05-122025-04-30https://publica.fraunhofer.de/handle/publica/48751210.1201/9781003532521-96The increasing complexity of industrial production demands innovative automation solutions. This paper presents a toolchain developed for the robust Sim2Real transfer of reinforcement learning-based control policies in robotic systems, addressing challenges in the inefficient manual programming of task-specific control. Our toolchain facilitates the training within a simulated environment in NVIDIA Isaac or MuJoCo and the subsequent implementation on real-world robotic hardware using ROS 2 for seamless interaction with low-level robot controllers. Several methods to ensure robust performance on real hardware are an integral part of our approach. The first real-world experiments of a dual-arm robotic assembly task demonstrate a 100% success rate, with process times closely matching those in simulations, verifying the model's robustness to uncertainties in system dynamics and sensor noise.enRoboticsMachine LearningReinforcement LearningSim2Real TransferToolchainIndustrial Assembly000 Informatik, Informationswissenschaft, allgemeine Werkeconference paper