Efficient grasping from RGBD images: Learning using a new rectangle representation

Given an image and an aligned depth map of an object, our goal is to estimate the full 7-dimensional gripper configuration—its 3D location, 3D orientation and the gripper opening width. Recently, learning algorithms have been successfully applied to grasp novel objects—ones not seen by the robot before. While these approaches use low-dimensional representations such as a ‘grasping point’ or a ‘pair of points’ that are perhaps easier to learn, they only partly represent the gripper configuration and hence are sub-optimal. We propose to learn a new ‘grasping rectangle’ representation: an oriented rectangle in the image plane. It takes into account the location, the orientation as well as the gripper opening…