Deep Learning Based 6-DoF Antipodal Grasp Planning from Point Cloud in Random Bin-Picking Task Using Single-View

Random bin picking is a crucial task in logistic cen- ters, which is driven by E-Commerce growth. In this letter, we present an end-to-end method for 6-DoF antipodal grasps from cluttered scenes. Our approach includes two main steps: find- ing Potential Grasp Areas (PGAs) from depth image of the bin and detecting suitable parallel grasps in PGAs from point cloud data. To support our work, the training datasets are generated automatically in Pybullet simulation environment including 5000 depth images and above 30000 point clouds of cluttered scenes with different number of objects, which save time significantly for collecting and labeling. We implemented real grasping experiments with a robot arm UR10, 2-finger gripper, depth camera L515, and 10 objects arranged randomly in the bin to evaluate the efficiency of this method. It is simple, fast, and efficient to deal with many kinds ofobjectwhicharerandominshape,dimension,pose,andmaterial.