  Learning generalizable skills in robotic manipulation has long been
challenging due to real-world sized observation and action spaces. One method
for addressing this problem is attention focus -- the robot learns where to
attend its sensors and irrelevant details are ignored. However, these methods
have largely not caught on due to the difficulty of learning a good attention
policy and the added partial observability induced by a narrowed window of
focus. This article addresses the first issue by constraining gazes to a
spatial hierarchy. For the second issue, we identify a case where the partial
observability induced by attention does not prevent Q-learning from finding an
optimal policy. We conclude with real-robot experiments on challenging
pick-place tasks demonstrating the applicability of the approach.
