GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation

Ruihai Wu, Ziyu Zhu, Yuran Wang, Yue Chen, Jiarui Wang, Hao Dong; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 6950-6959

Cluttered garments manipulation poses significant challenges in robotics due to the complex, deformable nature of garments and intricate garment relations. Unlike single-garment manipulation, cluttered scenarios require managing complex garment entanglements and interactions, while maintaining garment cleanliness and manipulation stability. To address these demands, we propose to learn point-level affordance, the dense representation modeling the complex space and multi-modal manipulation candidates, with novel designs for the awareness of garment geometry, structure, and inter-object relations. Additionally, we introduce an adaptation module, informed by learned affordance, to reorganize cluttered garments into configurations conducive to manipulation. Our framework demonstrates effectiveness over environments featuring diverse garment types and pile scenarios in both simulation and the real world. Project page: https://garmentpile.github.io/.