Publications Code Data The Dexterity Network (Dex-Net) is a research project including code, datasets, and algorithms for generating datasets of synthetic point clouds, robot parallel-jaw grasps and metrics of grasp robustness based on physics for thousands of 3D object models to train machine learning-based methods to plan robot grasps. The broader goal of the Dex-Net project is to develop highly reliable robot grasping across a wide variety of rigid objects such as tools, household items, packaged goods, and industrial parts. is designed to generated training datasets to learn Grasp Quality Convolutional Neural Networks (GQ-CNN) models that predict the probability of success of candidate parallel-jaw grasps on objects from point clouds. GQ-CNNs may be