Robot-aided cloth classification using depth information and CNNs

The final publication is available at link.springer.comWe present a system to deal with the problem of classifying garments from a pile of clothes. This system uses a robot arm to extract a garment and show it to a depth camera. Using only depth images of a partial view of the garment as input, a deep convolutional neural network has been trained to classify different types of garments. The robot can rotate the garment along the vertical axis in order to provide different views of the garment to enlarge the prediction confidence and avoid confusions. In addition to obtaining very high classification scores, compared to previous approaches to cloth classification that match the sensed data against a database, our system provides a fast and occlusion-robust solution to the problem.Peer ReviewedPostprint (author's final draft

Robot-aided cloth classification using depth information and CNNs

The final publication is available at link.springer.comWe present a system to deal with the problem of classifying garments from a pile of clothes. This system uses a robot arm to extract a garment and show it to a depth camera. Using only depth images of a partial view of the garment as input, a deep convolutional neural network has been trained to classify different types of garments. The robot can rotate the garment along the vertical axis in order to provide different views of the garment to enlarge the prediction confidence and avoid confusions. In addition to obtaining very high classification scores, compared to previous approaches to cloth classification that match the sensed data against a database, our system provides a fast and occlusion-robust solution to the problem.Peer Reviewe

Active garment recognition and target grasping point detection using deep learning

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Identification and bi-manual handling of deformable objects, like textiles, is one of the most challenging tasks in the field of industrial and service robotics. Their unpredictable shape and pose makes it very difficult to identify the type of garment and locate the most relevant parts that can be used for grasping. In this paper, we propose an algorithm that first, identifies the type of garment and second, performs a search of the two grasping points that allow a robot to bring the garment to a known pose. We show that using an active search strategy it is possible to grasp a garment directly from predefined grasping points, as opposed to the usual approach based on multiple re-graspings of the lowest hanging parts. Our approach uses a hierarchy of three Convolutional Neural Networks (CNNs) with different levels of specialization, trained both with synthetic and real images. The results obtained in the three steps (recognition, first grasping point, second grasping point) are promising. Experiments with real robots show that most of the errors are due to unsuccessful grasps and not to the localization of the grasping points, thus a more robust grasping strategy is required.Peer Reviewe