  In this paper we investigate how to effectively deploy deep learning in
practical industrial settings, such as robotic grasping applications. When a
deep-learning based solution is proposed, usually lacks of any simple method to
generate the training data. In the industrial field, where automation is the
main goal, not bridging this gap is one of the main reasons why deep learning
is not as widespread as it is in the academic world. For this reason, in this
work we developed a system composed by a 3-DoF Pose Estimator based on
Convolutional Neural Networks (CNNs) and an effective procedure to gather
massive amounts of training images in the field with minimal human
intervention. By automating the labeling stage, we also obtain very robust
systems suitable for production-level usage. An open source implementation of
our solution is provided, alongside with the dataset used for the experimental
evaluation.
