“In the paper we detail the Dexterity Network (Dex-Net) 2.0, a dataset of 6.7 million robust grasps and point clouds with synthetic noise generated from our probabilistic model of grasping rigid objects on a tabletop with a parallel-jaw gripper. We develop a deep Grasp Quality Convolutional Neural Network (GQ-CNN) model and train it on Dex-Net 2.0 to estimate grasp robustness from a candidate grasp and point cloud. We use the GQ-CNN to plan grasps on a physical robot by sampling a set of grasp candidates from an input point cloud with edge detection and executing the most robust grasp estimated by the GQ-CNN…”