Advances in industrial bin picking through sim-to-real transfer and cycle optimization

This work presents a novel method for addressing the bin picking problem with two significant contributions. First, we propose a specialized generator for synthetic labelled data tailored for bin picking applications. This synthetic data facilitates the training of deep learning models for detecting objects. Second, a parallel workflow to optimize cycle time is presented; this workflow leverages the robot’s motion to perform all cognitive computations. Experimental results demonstrate that models trained exclusively on virtual data achieve comparable performance when applied to real-world scenarios. Additionally, the proposed workflow effectively optimizes cycle time by ensuring uninterrupted robot movement throughout the process.