Learning to grasp unknown objects based on 3D edge information

In this work we refine an initial grasping behavior based on 3D edge information by learning. Based on a set of autonomously generated evaluated grasps and relations between the semi-global 3D edges, a prediction function is learned that computes a likelihood for the success of a grasp using either an offline or an online learning scheme. Both methods are implemented using a hybrid artificial neural network containing standard nodes with a sigmoid activation function and nodes with a radial basis function. We show that a significant performance improvement can be achieved.