Transfer Learning for Machine Learning-based Detection and Separation of Entanglements in Bin-Picking Applications

In this paper, we present a Domain Randomization and a Domain Adaptation approach to transfer experience for entanglement detection and separation from simulation into a real-world bin-picking application. We investigate the influence of different randomization options in image processing and use a CycleGAN as a further Domain Adaptation method to synthesize simulation data as realistically as possible. On the basis of this adapted data we re-train our detection and separation methods and validate the usefulness of these Sim-to-Real methods. In numerous real-world experiments we show that we achieve a significant increase of up to 71.74% in the performance of the overall system by using the Sim-to-Real approaches as opposed to the direct transfer.