Konrad, Anna and McDonald, John and Villing, Rudi (2023) GP-net: Flexible Viewpoint Grasp Proposal. 21st International Conference on Advanced Robotics (ICAR).

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Abstract

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/.

Item Type:	Article
Keywords:	grasping; robotics; neural networks; 6-DoF grasps; mobile manipulator; ROS;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Electronic Engineering
Item ID:	18255
Identification Number:	https://doi.org/10.1109/ICAR58858.2023.10406781
Depositing User:	John McDonald
Date Deposited:	07 Mar 2024 10:35
Journal or Publication Title:	21st International Conference on Advanced Robotics (ICAR)
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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