Learning Based Robotic Binpicking for Potentially Tangled Objects In this research, we tackle the challenge of picking only one object from a randomly stacked pile where the objects can potentially be tangled. No solution has been proposed to solve this challenge due to the complexity of picking one and only one object from the bin of tangled objects. Therefore, we propose a method for avoiding the situation where a robot picks multiple objects. In our proposed method, first, grasping pose candidates are computed by using the graspability index. Then, a Convolutional Neural Network (CNN) is trained to predict whether or not the robot can pick one and only one object from the bin. Additionally, since a physics simulator is used to collect data to train the CNN, an automatic picking system can be built. The effectiveness of the proposed method is confirmed through experiments on robot Nextage and compare with previous binpicking methods.

Depth Imagexe2x80x93Based Deep Learning of Grasp Planning for Textureless PlanarFaced Objects in VisionGuided Robotic BinPicking Binpicking of small parcels and other textureless planarfaced objects is a common task at warehouses. A general color imagexe2x80x93based visionguided robot picking system requires feature extraction and goal image preparation of various objects. However, feature extraction for goal image matching is difficult for textureless objects. Further, prior preparation of huge numbers of goal images is impractical at a warehouse. In this paper, we propose a novel depth imagexe2x80x93based visionguided robot binpicking system for textureless planarfaced objects. Our method uses a deep convolutional neural network (DCNN) model that is trained on 15,000 annotated depth images synthetically generated in a physics simulator to directly predict grasp points without object segmentation. Unlike previous studies that predicted grasp points for a robot suction hand with only one vacuum cup, our DCNN also predicts optimal grasp patterns for a hand with two vacuum cups (left cup on, right cup on, or both cups on). Further, we propose a surface feature descriptor to extract surface features (center position and normal) and refine the predicted grasp point position, removing the need for texture features for visionguided robot control and simtoreal modification for DCNN model training. Experimental results demonstrate the efficiency of our system, namely that a robot with 7 degrees of freedom can pick randomly posed textureless boxes in a cluttered environment with a 97.5% success rate at speeds exceeding 1000 pieces per hour.