GarmentPile：面向杂乱衣物操作的点级视觉可供性引导检索与适配
GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation
AI总结：
针对杂乱衣物操作中缠绕复杂、检索困难等问题，提出GarmentPile框架，通过学习点级视觉可供性结合适配模块重组衣物，在仿真和真实场景的多样衣物堆叠环境中验证了有效性。
摘要：
杂乱衣物操作因衣物复杂的可变形特性与衣物间的复杂关联带来了巨大挑战。与单衣物操作不同，杂乱场景需要处理复杂的衣物缠绕与相互作用，同时保持衣物整洁与操作稳定性。为满足这些需求，我们提出学习点级可供性——一种建模复杂空间与多模态操作候选的稠密表示，同时感知衣物几何、结构与物体间关系。此外，由于在部分极度缠绕的杂乱场景中直接检索衣物存在难度，我们引入了一个由学习到的可供性引导的适配模块，将高度缠绕的衣物重组为适合操作的状态。我们的框架在仿真与真实世界中、具有多样衣物类型与堆叠配置的环境下均展现出有效性。项目页面：https://garmentpile.github.io/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
