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/.

翻译：我们提出抓取提案网络(GP-net)，一种能够从灵活视点（例如移动操作机器人所经历的视点）生成六自由度抓取姿态的卷积神经网络模型。为训练GP-net，我们合成生成了包含深度图像和真实抓取信息的数据集。在真实世界实验中，我们使用EGAD评估基准，在PAL TIAGo移动操作机器人上将GP-net与两种常用算法——体积抓取网络(VGN)和抓取姿态检测包(GPD)——进行对比评估。区别于机器人抓取领域的最新方法，GP-net无需定义工作空间即可从灵活、未知视点抓取物体，并实现了54.4%的抓取成功率，而VGN和GPD的成功率分别为51.6%和44.2%。我们在https://aucoroboticsmu.github.io/GP-net/ 上提供了ROS软件包、代码及预训练模型。