  Recent developments in the field of robot grasping have shown great
improvements in the grasp success rates when dealing with unknown objects. In
this work we improve on one of the most promising approaches, the Grasp Quality
Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We
propose a new architecture for the GQ-CNN and describe practical improvements
that increase the model validation accuracy from 92.2% to 95.8% and from 85.9%
to 88.0% on respectively image-wise and object-wise training and validation
splits.
