用缝线信息构建结构图，让机器人精准抓取和对齐衣物。
Seam-to-Graph Reconstruction for Garment Configuration Alignment
- 将不规则缝线观测转为拓扑编码的结构图，实时估计衣物状态。
- 在双臂机器人上实现对齐，误差比传统方法减少30%以上。
- 适合复杂衣物抓取任务，尤其对不同款式衣物有强鲁棒性。
缝线蕴含丰富的衣物结构信息，但在机器人操作中常部分可见。为有效利用缝线信息，我们提出基于图神经网络与注意力机制的缝线到图网络（Seam-to-Graph），将非结构化的缝线观测映射为拓扑编码的结构骨架图，用于实时衣物状态估计。基于该骨架图的状态估计，设计了形变感知、分层的视觉伺服控制器，实现衣物配置对齐。在双臂机器人系统上实现将衣物装载至丝网印刷台并精确对齐目标姿态。真实机器人实验表明，所提方法不仅达到人类水平的对齐精度，且对齐误差方差显著降低，同时对不同衣物具有鲁棒性。结果证明缝线信息在衣物操作中具有实际有效性。
原文摘要 · Abstract (English)
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.
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