用拓扑图建模衣物折叠，实现复杂衣物自遮挡下的通用折叠控制。
Learning a General Model: Folding Clothing with Topological Dynamics
- 以可见褶皱为骨架构建低维拓扑图表示衣物状态
- 结合语义分割与关键点检测，解决自遮挡下结构解析难题
- 基于改进GNN预测形变并生成控制所需的雅可比矩阵
衣物高自由度和复杂结构给操作带来挑战。本文提出一种通用的拓扑动力学模型来折叠复杂衣物。通过将可见褶皱结构作为拓扑骨架，设计新型拓扑图表示衣物状态，该图维度低，适用于多种折叠状态，能反映衣物约束并预测其运动。为从自遮挡中提取拓扑图，采用语义分割分析遮挡关系，分解衣物结构，并结合关键点检测生成拓扑图。通过改进的图神经网络（GNN）学习通用动力学，可预测衣物形变，并用于计算形变雅可比矩阵以实现控制。实验使用夹克验证了算法在识别和折叠复杂自遮挡衣物方面的有效性。
原文摘要 · Abstract (English)
The high degrees of freedom and complex structure of garments present significant challenges for clothing manipulation. In this paper, we propose a general topological dynamics model to fold complex clothing. By utilizing the visible folding structure as the topological skeleton, we design a novel topological graph to represent the clothing state. This topological graph is low-dimensional and applied for complex clothing in various folding states. It indicates the constraints of clothing and enables predictions regarding clothing movement. To extract graphs from self-occlusion, we apply semantic segmentation to analyze the occlusion relationships and decompose the clothing structure. The decomposed structure is then combined with keypoint detection to generate the topological graph. To analyze the behavior of the topological graph, we employ an improved Graph Neural Network (GNN) to learn the general dynamics. The GNN model can predict the deformation of clothing and is employed to calculate the deformation Jacobi matrix for control. Experiments using jackets validate the algorithm's effectiveness to recognize and fold complex clothing with self-occlusion.
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