  Capturing and labeling camera images in the real world is an expensive task,
whereas synthesizing labeled images in a simulation environment is easy for
collecting large-scale image data. However, learning from only synthetic images
may not achieve the desired performance in the real world due to a gap between
synthetic and real images. We propose a method that transfers learned detection
of an object position from a simulation environment to the real world. This
method uses only a significantly limited dataset of real images while
leveraging a large dataset of synthetic images using variational autoencoders.
Additionally, the proposed method consistently performed well in different
lighting conditions, in the presence of other distractor objects, and on
different backgrounds. Experimental results showed that it achieved accuracy of
1.5mm to 3.5mm on average. Furthermore, we showed how the method can be used in
a real-world scenario like a "pick-and-place" robotic task.
