Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc. Perception is considered one of the major challenges in DOM due to the complex dynamics and high degree of freedom of deformable objects. In this paper, we develop a novel image-processing algorithm based on Gabor filters to extract useful features from cloth, and based on this, devise a strategy for cloth flattening tasks. We also evaluate the overall framework experimentally and compare it with three human operators. The results show that our algorithm can determine the direction of wrinkles on the cloth accurately in simulation as well as in real robot experiments. Furthermore, our dewrinkling strategy compares favorably to baseline methods. The experiment video is available on https://sites.google.com/view/robotic-fabric-flattening/home

翻译：可变形物体操作（DOM）是重要的研究领域，因其在自动布料处理、线缆布线、外科手术等实际任务中具有重要应用。由于可变形物体的复杂动力学特性和高自由度，感知被认为是DOM的主要挑战之一。本文基于Gabor滤波器开发了一种新型图像处理算法，用于从布料中提取有效特征，并据此设计了布料展平策略。我们通过实验评估了整体框架，并与三名人类操作员进行了对比。结果表明，该算法在仿真环境和真实机器人实验中均能准确判断布料的褶皱方向。此外，我们的去皱策略优于基线方法。实验视频详见 https://sites.google.com/view/robotic-fabric-flattening/home