利用缝线信息指导机器人抓取和展开T恤，提升操作成功率。
SIS: Seam-Informed Strategy for T-shirt Unfolding
- 通过缝线特征提取法识别适合抓取的点位
- 基于人类示范与机器人执行反馈迭代优化决策矩阵
- 全程使用真实数据训练，无需仿真环境
缝线是服装中富含信息的结构，不同类型的缝线及其组合可帮助选择抓取点以实现衣物操作。本文提出一种新的缝线感知策略（SIS），用于指导机器人完成抓取和展开T恤等操作。采用提出的缝线特征提取方法（SFEM）提取双臂机械臂抓取点候选位置，并通过决策矩阵迭代法（DMIM）从中选择最优抓取对。决策矩阵初始由多人示范生成，后续根据机器人实际执行结果动态更新，持续提升抓取与展开性能。所提方案完全基于真实数据训练，不依赖仿真。实验验证了该策略的有效性。项目视频见 https://github.com/lancexz/sis。
原文摘要 · Abstract (English)
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
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