用神经网络直接生成布料抚平动作，速度快且效果好
Automated Action Generation based on Action Field for Robotic Garment Smoothing and Alignment
- 输入图像直接生成每个像素的机械臂动作向量
- 比之前方法更快，仿真中平整度和对齐精度更高
- 适合各类布料的自动化处理，真实场景表现稳定
由于服装形状多样且易变形，机器人进行布料操作极具挑战。本文提出一种新型机器人布料抚平与对齐方法，相比以往方法显著提升精度并降低计算时间。该方法采用动作生成器，通过神经网络直接从场景图像解析出像素级末端执行器动作向量，并预测操作评分图以排序潜在动作，从而选择最优操作。大量仿真实验表明，该方法在平整度和对齐性能上优于先前方法，同时计算速度更快。真实世界实验显示，该方法对不同类型的服装具有良好泛化能力，能成功实现布料平整。
原文摘要 · Abstract (English)
Garment manipulation using robotic systems is a challenging task due to the diverse shapes and deformable nature of fabric. In this paper, we propose a novel method for robotic garment smoothing and alignment that significantly improves the accuracy while reducing computational time compared to previous approaches. Our method features an action generator that directly interprets scene images and generates pixel-wise end-effector action vectors using a neural network. The network also predicts a manipulation score map that ranks potential actions, allowing the system to select the most effective action. Extensive simulation experiments demonstrate that our method achieves higher smoothing and alignment performances and faster computation time than previous approaches. Real-world experiments show that the proposed method generalizes well to different garment types and successfully flattens garments.
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