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Deep learning provides a powerful framework for automated acquisition ofcomplex robotic motions. However, despite a certain degree of generalization,the need for vast amounts of training data depending on the work-objectposition is an obstacle to industrial applications. Therefore, a robotmotion-generation model that can respond to a variety of work-object positionswith a small amount of training data is necessary. In this paper, we propose amethod robust to changes in object position by automatically extracting spatialattention points in the image for the robot task and generating motions on thebasis of their positions. We demonstrate our method with an LBR iiwa 7R1400robot arm on a picking task and a pick-and-place task at various positions invarious situations. In each task, the spatial attention points are obtained forthe work objects that are important to the task. Our method is robust tochanges in object position. Further, it is robust to changes in background,lighting, and obstacles that are not important to the task because it onlyfocuses on positions that are important to the task.

Spatial Attention Point Network for Deep-learning-based Robust Autonomous Robot Motion Generation