  A simple gripper can solve more complex manipulation tasks if it can utilize
the external environment such as pushing the object against the table or a
vertical wall, known as "Extrinsic Dexterity." Previous work in extrinsic
dexterity usually has careful assumptions about contacts which impose
restrictions on robot design, robot motions, and the variations of the physical
parameters. In this work, we develop a system based on reinforcement learning
(RL) to address these limitations. We study the task of "Occluded Grasping"
which aims to grasp the object in configurations that are initially occluded;
the robot needs to move the object into a configuration from which these grasps
can be achieved. We present a system with model-free RL that successfully
achieves this task using a simple gripper with extrinsic dexterity. The policy
learns emergent behaviors of pushing the object against the wall to rotate and
then grasp it without additional reward terms on extrinsic dexterity. We
discuss important components of the system including the design of the RL
problem, multi-grasp training and selection, and policy generalization with
automatic curriculum. Most importantly, the policy trained in simulation is
zero-shot transferred to a physical robot. It demonstrates dynamic and
contact-rich motions with a simple gripper that generalizes across objects with
various size, density, surface friction, and shape with a 78% success rate.
Videos can be found at https://sites.google.com/view/grasp-ungraspable/.
