In this post, I want to review a technique which works directly with point clouds to detect a grasp configuration. By grasp configuration, I mean the position and orientation of the gripper. The following picture shows a general overview of the approach. To summarize, the key contributions of this work are: • Proposing a network to evaluate the grasp quality by performing geometry analysis directly from a 3D point cloud based on the network architecture of PointNet. Compared with other CNN-based methods, this method can exploit the 3D geometry information in the depth image better without any hand-crafted features and sustain a relatively small amount of parameters for learning and inference efficiency.