  Deep learning provides a powerful framework for automated acquisition of
complex robotic motions. However, despite a certain degree of generalization,
the need for vast amounts of training data depending on the work-object
position is an obstacle to industrial applications. Therefore, a robot
motion-generation model that can respond to a variety of work-object positions
with a small amount of training data is necessary. In this paper, we propose a
method robust to changes in object position by automatically extracting spatial
attention points in the image for the robot task and generating motions on the
basis of their positions. We demonstrate our method with an LBR iiwa 7R1400
robot arm on a picking task and a pick-and-place task at various positions in
various situations. In each task, the spatial attention points are obtained for
the work objects that are important to the task. Our method is robust to
changes in object position. Further, it is robust to changes in background,
lighting, and obstacles that are not important to the task because it only
focuses on positions that are important to the task.
