Region-Aware 6D Grasping for Industrial Bin-Picking: A Sim2Real Label Self-Generation and Hybrid Evaluation Framework

Abstract

The integration of high-quality datasets, a generalized network model, and robust evaluation strategies sets a significant benchmark for advancing policy development in industrial bin-picking. This paper introduces the concept of region-aware grasping, a cutting-edge simulation to reality system designed to generate and evaluate 6D poses, empowering robots to grasp novel workpieces in stacked environments. The proposed system comprises two core components: the Sim2Real dataset, a large-scale synthetic point cloud dataset for grasp analysis, and Semantic-GraspNet, a policy framework that predicts full 6D grasp poses for stacked objects. By encoding and decoding point cloud data, Semantic-GraspNet innovatively transforms the pose prediction into a semantic categorization problem. Furthermore, we present a hybrid evaluation strategy that integrates pose assessment with mechanical grasp performance analysis, thereby enhancing both grasp success rates and sorting efficiency. To extend its capabilities, Semantic-GraspNet is combined with multi-modal large models, enabling accurate object-category-specific grasping in complex bin-picking scenarios. In real-world industrial applications, the system achieves a grasp completion rate of 91.3% in cluttered scenes and 89.2% in densely stacked environments, showcasing state-of-the-art performance in robotic picking and placing tasks.