Generalizable Category-Level Topological Structure Learning for Clothing Recognition in Robotic Grasping

Abstract

Recognizing various types of clothing is crucial for robotic clothing manipulation tasks, such as garment organization and robot-assisted dressing. Unlike rigid object recognition, clothing recognition remains a challenging task due to the diverse forms introduced by flexible deformations. However, existing classification models primarily focus on clothing color and texture while overlooking structural features, limiting their ability to distinguish between deformable clothing categories with similar color and texture. Moreover, due to the insufficient representation of structural features, these models heavily rely on manually annotated labels, making it difficult to accurately recognize unseen clothing items with new colors or textures. To address these challenges, we propose a novel topological structure representation and optimization strategy for category-level clothing structural feature learning. Additionally, we design a multi-clothing classification framework based on multiple mask generation to identify clothing regions within a scene. By leveraging our proposed structural feature learning strategy, our framework effectively generalizes to unseen clothing items. Finally, we introduce a fabric-specific grasping position estimation method and develop a corresponding robotic grasping system capable of selecting and grasping specified clothing items based on user instructions. Extensive real-world robotic experiments demonstrate the effectiveness of our system, and comprehensive comparisons with multiple baselines further validate the superiority of our approach.