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）是机器人领域的重要研究方向，其在自动布料处理、线缆布线、外科手术等实际任务中具有广泛应用。由于可变形物体的复杂动力学特性与高自由度特性，感知能力被认为是DOM面临的主要挑战之一。本文提出一种基于Gabor滤波器的创新图像处理算法，用于提取布料的有效特征，并据此设计了一种布料展平策略。我们通过实验对整体框架进行评估，并与三位人类操作员进行对比。实验结果表明：在仿真环境与真实机器人实验中，该算法均能准确检测布料褶皱方向。此外，与其它基线方法相比，采用本文算法生成的去皱策略进行展平操作的机器人取得了令人满意的性能表现。实验视频请访问：https://sites.google.com/view/robotic-fabric-flattening/home