  We present a robotic system for picking a target from a pile of objects that
is capable of finding and grasping the target object by removing obstacles in
the appropriate order. The fundamental idea is to segment instances with both
visible and occluded masks, which we call `instance occlusion segmentation'. To
achieve this, we extend an existing instance segmentation model with a novel
`relook' architecture, in which the model explicitly learns the inter-instance
relationship. Also, by using image synthesis, we make the system capable of
handling new objects without human annotations. The experimental results show
the effectiveness of the relook architecture when compared with a conventional
model and of the image synthesis when compared to a human-annotated dataset. We
also demonstrate the capability of our system to achieve picking a target in a
cluttered environment with a real robot.
