Deep Learning Based 6-DoF Antipodal Grasp Planning From Point Cloud in Random Bin-Picking Task Using Single-View
Tat Hieu Bui, Yeong Gwang Son, Seung Jae Moon, Quang Huy Nguyen, Issac Rhee, Ju Hong, Hyouk Ryeol Choi
Abstract
Random bin picking is a crucial task in logistic centers, 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: finding 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 30 000 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 of object which are random in shape, dimension, pose, and material.