通过点级视觉可操作性，实现乱堆衣物的智能抓取与重构。
GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation
- 基于点级可操作性建模，捕捉衣物形变与交互关系。
- 在仿真与真实场景中成功处理多种堆叠形态的衣物。
- 适合机器人抓取、智能仓储等复杂物体操作任务。
乱堆衣物操作因衣物复杂的柔性和纠缠关系而极具挑战。与单件衣物操作不同，乱堆场景需处理复杂的缠绕与相互作用，同时保持衣物整洁与操作稳定性。为此，我们提出学习点级可操作性，即密集表征复杂空间及多模态操作候选，同时考虑衣物几何、结构和物间关系。此外，针对极复杂缠绕难以直接抓取的问题，引入由学习到的可操作性引导的适应模块，将高度纠缠的衣物重排至可操作状态。该框架在包含多种衣物类型和堆叠配置的仿真与真实环境中均表现出色。
原文摘要 · Abstract (English)
Cluttered garments manipulation poses significant challenges 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, while being aware of garment geometry, structure, and inter-object relations. Additionally, as it is difficult to directly retrieve a garment in some extremely entangled clutters, we introduce an adaptation module, guided by learned affordance, to reorganize highly-entangled garments into states plausible for manipulation. Our framework demonstrates effectiveness over environments featuring diverse garment types and pile configurations in both simulation and the real world. Project page: https://garmentpile.github.io/.
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