Seams encode rich structural information about garments but are frequently partially observable in robotic manipulation scenarios. To robustly leverage seam information, we propose a Seam-to-Graph network based on graph neural networks and attention mechanisms. This network maps unstructured seam observations to a topology-encoded structural skeleton graph for real-time garment state estimation. Using this skeleton-graph-based state estimation, we design a deformation-aware, hierarchical visual servoing controller for garment configuration alignment. We implement this controller on a bimanual robot system to load a garment onto a screen printing platen and to align it to the desired configuration precisely. Real-robot experiments demonstrate that the robot using the proposed method not only achieves human-level alignment accuracy with reduced variance in alignment error but is also robust to different garments. These results demonstrate that the use of seam information is effective for garment manipulation.

翻译：接缝编码了服装丰富的结构信息，但在机器人操作场景中通常仅部分可观测。为稳健利用接缝信息，我们提出一种基于图神经网络与注意力机制的Seam-to-Graph网络。该网络将非结构化接缝观测映射为拓扑编码的结构骨架图，用于实时服装状态估计。基于此骨架图状态估计，我们设计了变形感知分层视觉伺服控制器以实现服装构型对齐。将该控制器部署于双臂机器人系统，完成将服装加载至丝网印刷台并精确对齐至目标构型的任务。真实机器人实验表明：采用所提方法的机器人不仅达到人类水平的对齐精度（且对齐误差方差更低），还对不同服装具有鲁棒性。这些结果证明了接缝信息在服装操作中的有效性。