  Robotic grasp detection task is still challenging, particularly for novel
objects. With the recent advance of deep learning, there have been several
works on detecting robotic grasp using neural networks. Typically, regression
based grasp detection methods have outperformed classification based detection
methods in computation complexity with excellent accuracy. However,
classification based robotic grasp detection still seems to have merits such as
intermediate step observability and straightforward back propagation routine
for end-to-end training. In this work, we propose a novel classification based
robotic grasp detection method with multiple-stage spatial transformer networks
(STN). Our proposed method was able to achieve state-of-the-art performance in
accuracy with real- time computation. Additionally, unlike other regression
based grasp detection methods, our proposed method allows partial observation
for intermediate results such as grasp location and orientation for a number of
grasp configuration candidates.
