Seam-to-Graph Reconstruction for Garment Configuration Alignment
This work addresses the problem of precise garment manipulation for robotic systems, particularly in manufacturing contexts like screen printing, by leveraging seam information for state estimation.
The paper proposes a Seam-to-Graph network using graph neural networks and attention mechanisms to map partial seam observations to a structural skeleton graph for real-time garment state estimation, enabling a deformation-aware hierarchical visual servoing controller for garment configuration alignment. In real-robot experiments, the method achieves human-level alignment accuracy with reduced variance and robustness across different garments.
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.