  Pose estimation is the task of determining the 6D position of an object in a
scene. Pose estimation aid the abilities and flexibility of robotic set-ups.
However, the system must be configured towards the use case to perform
adequately. This configuration is time-consuming and limits the usability of
pose estimation and, thereby, robotic systems. Deep learning is a method to
overcome this configuration procedure by learning parameters directly from the
dataset. However, obtaining this training data can also be very time-consuming.
The use of synthetic training data avoids this data collection problem, but a
configuration of the training procedure is necessary to overcome the domain gap
problem. Additionally, the pose estimation parameters also need to be
configured. This configuration is jokingly known as grad student descent as
parameters are manually adjusted until satisfactory results are obtained. This
paper presents a method for automatic configuration using only synthetic data.
This is accomplished by learning the domain randomization during network
training, and then using the domain randomization to optimize the pose
estimation parameters. The developed approach shows state-of-the-art
performance of 82.0 % recall on the challenging OCCLUSION dataset,
outperforming all previous methods with a large margin. These results prove the
validity of automatic set-up of pose estimation using purely synthetic data.
