Cloth manipulation based on category classification and landmark detection. (21st July 2022)

• Record Type: Journal Article
• Authors: Gustavsson, Oscar; Ziegler, Thomas; Welle, Michael C; Bütepage, Judith; Varava, Anastasiia; Kragic, Danica
• Is Part Of: International journal of advanced robotic systems. Volume 19:Number 4(2022)
• Publication Date: 2022-07-21
• Subjects: Cloth/garment manipulation -- classification -- vision for robotics -- data augmentation; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/17298806221110445
• Languages: English
• ISSNs: 1729-8806

Abstract

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.