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 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 the simulation as well as the real robot experiments. Besides, the robot executing the flattening tasks using the dewrinkling strategy given by our algorithm achieves satisfying performance compared to other 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，因其有助于自动布料处理、电缆布线、外科手术等实际任务。感知被认为是DOM中的主要挑战之一，这是由于可变形物体复杂的动力学特性和高自由度。本文基于Gabor滤波器开发了一种新颖的图像处理算法，以从布料中提取有用特征，并据此设计了布料展平任务的策略。我们通过实验评估了整体框架，并将其与三名人类操作员进行了比较。结果表明，我们的算法在仿真和真实机器人实验中均能准确确定布料褶皱方向。此外，使用我们算法提供的去皱策略执行展平任务的机器人，相比其他基线方法取得了令人满意的性能。实验视频可访问 https://sites.google.com/view/robotic-fabric-flattening/home 获取。