Cloth manipulation remains a challenging problem for the robotic community. Recently, there has been an increased interest in applying deep learning techniques to problems in the fashion industry. As a result, large annotated data sets for cloth category classification and landmark detection were created. In this work, we leverage these advances in deep learning to perform cloth manipulation. We propose a full cloth manipulation framework that, performs category classification and landmark detection based on an image of a garment, followed by a manipulation strategy. The process is performed iteratively to achieve a stretching task where the goal is to bring a crumbled cloth into a stretched out position. We extensively evaluate our learning pipeline and show a detailed evaluation of our framework on different types of garments in a total of 140 recorded and available experiments. Finally, we demonstrate the benefits of training a network on augmented fashion data over using a small robotic-specific data set.

对于机器人社区来说，布料操作仍然是一个具有挑战性的问题。最近，人们越来越关注将深度学习技术应用于时尚行业的问题。结果，创建了用于布料类别分类和地标检测的大型注释数据集。在这项工作中，我们利用深度学习的这些进步来执行布料操作。我们提出了一个完整的布料操作框架，该框架基于服装图像执行类别分类和地标检测，然后是操作策略。该过程反复执行以实现拉伸任务，其目标是将碎布带入拉伸位置。我们广泛评估了我们的学习管道，并在总共 140 个记录和可用的实验中展示了我们对不同类型服装的框架的详细评估。最后，我们展示了在增强时尚数据上训练网络比使用小型机器人特定数据集的好处。