Seam-to-Graph Reconstruction for Garment Configuration Alignment

2026-06-13

AI Summary

This work addresses the challenge of partial seam information loss in garment manipulation, which hinders accurate state estimation and alignment. To overcome this, the authors propose Seam-to-Graph, a novel framework that, for the first time, converts unstructured seam observations into a topology-encoded skeletal graph. By integrating graph neural networks with attention mechanisms, the method enables robust, real-time state estimation. Furthermore, a deformation-aware hierarchical visual servoing controller is designed to guide a dual-arm robot in achieving precise configuration alignment. Evaluated across diverse garments, the approach attains human-level alignment accuracy, substantially reduces error variance, and demonstrates strong generalizability and robustness.

Abstract

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.

Problem

• seam observation
• garment manipulation
• configuration alignment
• partial observability
• structural information

Innovation

• Seam-to-Graph
• graph neural networks
• attention mechanism
• deformation-aware visual servoing
• garment manipulation