GP-net: Flexible Viewpoint Grasp Proposal

• Main Authors: Konrad, Anna, McDonald, John, Villing, Rudi
• Format: Journal Article
• Language: English
• Published: 21-09-2022
• Subjects: Computer Science - Learning; Computer Science - Robotics
• DOI: 10.48550/arxiv.2209.10404

We present the Grasp Proposal Network (GP-net), a Convolutional Neural
Network model which can generate 6-DoF grasps from flexible viewpoints, e.g. as
experienced by mobile manipulators. To train GP-net, we synthetically generate
a dataset containing depth-images and ground-truth grasp information. In
real-world experiments, we use the EGAD evaluation benchmark to evaluate GP-net
against two commonly used algorithms, the Volumetric Grasping Network (VGN) and
the Grasp Pose Detection package (GPD), on a PAL TIAGo mobile manipulator. In
contrast to the state-of-the-art methods in robotic grasping, GP-net can be
used for grasping objects from flexible, unknown viewpoints without the need to
define the workspace and achieves a grasp success of 54.4% compared to 51.6%
for VGN and 44.2% for GPD. We provide a ROS package along with our code and
pre-trained models at https://aucoroboticsmu.github.io/GP-net/.