OPTRObot: A Synthetic Training Paradigm for Robotic Grasping of Specular Objects in Cluttered Environments

We present a novel and complete vision-based pipeline for 6DoF object pose estimation in challenging industrial bin-picking scenarios, characterized by significant clutter, occlusions, and reflective surfaces. Our approach addresses the limitations of both computationally expensive fine-tuning methods and the current immaturity of foundation models in handling such complex environments. The key contribution lies in a balanced approach leveraging synthetic data augmentation and a streamlined architecture to achieve robust performance without extensive per-object optimization. The pipeline integrates existing state-of-the-art object detection, coarse pose estimation, and a render-and-compare refinement strategy, enabling accurate pose estimation from monocular images. We introduce a new benchmark on the recently released dataset, establishing a baseline for future research. Unlike existing industrial approaches, our system minimizes reliance on multi-sensor configurations, offering a cost-effective and easily deployable solution. We demonstrate the impact of error propagation across a complete pipeline using datasets that mirror real-world industrial conditions, in contrast to commonly used but less representative datasets.