SIS: Seam-Informed Strategy for T-Shirt Unfolding
Published:
X. Huang1, 3, A. Seino1, 2, F. Tokuda1, 2, A. Kobayashi1, 2, D. Chen 4, Y. Hirata 4, N. C. Tien1, 2, K. Kosuge1, 2, 3
1. Centre for Transformative Garment Production, Unites 1215 to 1220, 12/F, Building 19W, SPX1, Hong Kong Schience Park, Pak Shek Kok, N. T., Hong Kong SAR
2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR
3. Director of the JC STEM Lab of Robotics forSoft Materials, Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong SAR
4. Department of Robotics, Graduate School of Engineering, Tohoku University, Japan
Published in IEEE Robotics and Automation Letters (RA-L), 7 (2025) 7342 - 7349
DOI: 10.1109/LRA.2025.3574966
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- Abstract
- Seams are information-rich components of garments. The presence of different types of seams and their combinations helps to select grasping points for garment handling. In this letter, we propose a new Seam-Informed Strategy (SIS) for finding actions for handling a garment, such as grasping and unfolding a T-shirt. Candidates for a pair of grasping points for a dual-arm manipulator system are extracted using the proposed Seam Feature Extraction Method (SFEM). A pair of grasping points for the robot system is selected by the proposed Decision Matrix Iteration Method (DMIM). The decision matrix is first computed by multiple human demonstrations and updated by the robot execution results to improve the grasping and unfolding performance of the robot. Note that the proposed scheme is trained on real data without relying on simulation. Experimental results demonstrate the effectiveness and generalization ability of the proposed strategy.
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