  We present the Versatile Grasp Quality Convolutional Neural Network
(VGQ-CNN), a grasp quality prediction network for 6-DOF grasps. VGQ-CNN can be
used when evaluating grasps for objects seen from a wide range of camera poses
or mobile robots without the need to retrain the network. By defining the grasp
orientation explicitly as an input to the network, VGQ-CNN can evaluate 6-DOF
grasp poses, moving beyond the 4-DOF grasps used in most image-based grasp
evaluation methods like GQ-CNN. To train VGQ-CNN, we generate the new Versatile
Grasp dataset (VG-dset) containing 6-DOF grasps observed from a wide range of
camera poses. VGQ-CNN achieves a balanced accuracy of 82.1% on our test-split
while generalising to a variety of camera poses. Meanwhile, it achieves
competitive performance for overhead cameras and top-grasps with a balanced
accuracy of 74.2% compared to GQ-CNN's 76.6%. We also propose a modified
network architecture, FAST-VGQ-CNN, that speeds up inference using a shared
encoder architecture and can make 128 grasp quality predictions in 12ms on a
CPU. Code and data are available at https://aucoroboticsmu.github.io/vgq-cnn/.
