SIS: Seam-Informed Strategy for T-shirt Unfolding

Xuzhao Huang, Akira Seino, Fuyuki Tokuda, Akinari Kobayashi, Dayuan Chen, Yasuhisa Hirata, Norman C. Tien, Kazuhiro Kosuge
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引用次数: 0

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 paper, 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 of the proposed strategy. The project video is available at https://github.com/lancexz/sis.
SIS: T恤展开的接缝信息战略
接缝是服装中信息丰富的组成部分。不同类型接缝的存在及其组合有助于选择服装处理的抓取点。在本文中,我们提出了一种新的 "接缝信息策略"(Seam-Informed Strategy,SIS),用于寻找处理服装的动作,如抓取和展开衬衫。利用提出的接缝特征提取方法(SFEM)提取双臂机械手系统的一对抓取点候选。决策矩阵首先由多次人类示范计算得出,然后根据机器人的执行结果进行更新,以提高机器人的抓取和展开性能。实验结果证明了所提策略的有效性。项目视频请访问 https://github.com/lancexz/sis。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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