  We propose a novel formulation of robotic pick and place as a deep
reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic
manipulation frame the problem in terms of low level states and actions, we
propose a more abstract formulation. In this formulation, actions are target
reach poses for the hand and states are a history of such reaches. We show this
approach can solve a challenging class of pick-place and regrasping problems
where the exact geometry of the objects to be handled is unknown. The only
information our method requires is: 1) the sensor perception available to the
robot at test time; 2) prior knowledge of the general class of objects for
which the system was trained. We evaluate our method using objects belonging to
two different categories, mugs and bottles, both in simulation and on real
hardware. Results show a major improvement relative to a shape primitives
baseline.
