6-DOF Grasp Detection for Unknown Objects

Abstract

Reliable robotic grasping of a prior unknown object requires a three-dimensional volumetric scene. Recent successful approaches use convolutional neural networks to find grasp candidates in depth images. We propose to expand this strategy by using multiple real and virtual viewpoints and projecting predicted grasp quality information to a surface representation of the object. This allows us to find 6-DOF grasp poses for arbitrary, unknown objects.