  Robotic learning in simulation environments provides a faster, more scalable,
and safer training methodology than learning directly with physical robots.
Also, synthesizing images in a simulation environment for collecting
large-scale image data is easy, whereas capturing camera images in the real
world is time consuming and expensive. However, learning from only synthetic
images may not achieve the desired performance in real environments due to the
gap between synthetic and real images. We thus propose a method that transfers
learned capability of detecting object position from a simulation environment
to the real world. Our method enables us to use only a very limited dataset of
real images while leveraging a large dataset of synthetic images using multiple
variational autoencoders. It detects object positions 6 to 7 times more
precisely than the baseline of directly learning from the dataset of the real
images. Object position estimation under varying environmental conditions forms
one of the underlying requirement for standard robotic manipulation tasks. We
show that the proposed method performs robustly in different lighting
conditions or with other distractor objects present for this requirement. Using
this detected object position, we transfer pick-and-place or reaching tasks
learned in a simulation environment to an actual physical robot without
re-training.
