CPQNet: Contact Points Quality Network for Robotic Grasping

In typical data-based grasping methods, a grasp based on parallel-jaw grippers is parameterized by the center of the gripper, the rotation angle, and the gripper opening width so as to predict the quality and pose of grasps at every pixel. In contrast, a grasp is represented using only two contact points for contact-points-based grasp representation, which allows for fusion with tactile sensors more naturally. In this work, we propose a method using contact-points-based grasp representation to get a robust grasp using only one contact points quality map generated by a neural network, which significantly reduces the complexity of the network with fewer parameters. We provide a synthetic dataset including depth image and contact points quality map generated by thousands of 3D models. We also provide the method for data generation, which can be used for contact-points-based multi-fingers grasp. Experiments show that contact points quality network can plan an available grasp in 0.15 seconds. The grasping success rate for unknown household objects is 94%. Our method is also available for deformable objects with a success rate of 95%. The dataset and reference code can be found on the project website: https://sites.google.com/view/cpqnet.