Detecting layered structures of partially occluded objects for bin picking When robots engage in bin picking of multiple objects, a failure in grasping partially occluded objects may occur because other objects may overlap the desired ones. Therefore, the layered structure of objects needs to be detected, and the picking order needs to be established. In this paper, we propose a new dataset that evaluates not only the area of objects but also the layered structures of objects. In this dataset, three tasks are targeted: object detection, semantic segmentation, and segmentation of occluded areas for bin picking of multiple objects. The dataset, called xe2x80x9cthe Amazon Robotics Challenge (ARC) Multitask Datasetxe2x80x9d contains 1,500 RGB images and depth images, including all scenes containing bounding box labels, semantic segmentation labels, and occluded area labels. This enables representing the layered structure of overlapped objects with a tree structure. A benchmark of the ARC multitask dataset demonstrated that occluded areas could be segmented using a Mask regional convolutional neural network (RCNN) and that layered structures of objects could be predicted. Our dataset is available at the following URL:http:mprg.jpresearcharc_dataset_2017_e.

Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions Semantic segmentation and instance level segmentation made substantial progress in recent years due to the emergence of deep neural networks (DNNs). A number of deep architectures with Convolution Neural Networks (CNNs) were proposed that surpass the traditional machine learning approaches for segmentation by a large margin. These architectures predict the directly observable semantic category of each pixel by usually optimizing a crossentropy loss. In this work we push the limit of semantic segmentation towards predicting semantic labels of directly visible as well as occluded objects or objects parts, where the networkxe2x80x99s input is a single depth image. We group the semantic categories into one background and multiple foreground object groups, and we propose a modification of the standard crossentropy loss to cope with the settings. In our experiments we demonstrate that a CNN trained by minimizing the proposed loss is able to predict semantic categories for visible and occluded object parts without requiring to increase the network size (compared to a standard segmentation task). The results are validated on a newly generated dataset (augmented from SUNCG) dataset.