Grasping like humans: a stacked-fabric grasp localization strategy based on convex structure perception and hierarchical fuzzy decision-making

Abstract

Due to the complex structures, variable morphologies, and multiple feasible grasping strategies for entangled fabrics during stacked sorting, achieving stable robotic grasping in such scenarios remains highly challenging. This study formulates the stacked-fabric grasping task as a problem of structural perception and multi-attribute decision-making. A stacked-fabric grasping method is proposed. Specifically, a convex-structure perception method based on point cloud geometric features is developed to extract operable regions from point cloud data, and a set of candidate grasp poses is generated based on these structures. Furthermore, to address the difficulty of effectively evaluating candidate poses, a hierarchical fuzzy neural network is developed by simulating the human fuzzy decision-making process, enabling interpretable evaluation and selection of the optimal grasp pose. Comparative experiments are conducted on a constructed fabric grasping dataset. Compared with the best-performing baseline model, the proposed method improves the prediction accuracy of grasp feasibility, grasp separation effectiveness, and grasp reliability by 1.3%, 0.3%, and 0.7%, respectively. Robotic grasping experiments further show that the proposed method achieves a grasping success rate of 93.3% in randomly stacked scenarios. The results demonstrate that integrating convex structure perception with hierarchical fuzzy decision-making provides an effective and interpretable strategy for grasp pose localization and selection in stacked-fabric grasping.