  We study an emerging problem named "grasping the invisible" in robotic
manipulation, in which a robot is tasked to grasp an initially invisible target
object via a sequence of pushing and grasping actions. In this problem, pushes
are needed to search for the target and rearrange cluttered objects around it
to enable effective grasps. We propose to solve the problem by formulating a
deep learning approach in a critic-policy format. The target-oriented motion
critic, which maps both visual observations and target information to the
expected future rewards of pushing and grasping motion primitives, is learned
via deep Q-learning. We divide the problem into two subtasks, and two policies
are proposed to tackle each of them, by combining the critic predictions and
relevant domain knowledge. A Bayesian-based policy accounting for past action
experience performs pushing to search for the target; once the target is found,
a classifier-based policy coordinates target-oriented pushing and grasping to
grasp the target in clutter. The motion critic and the classifier are trained
in a self-supervised manner through robot-environment interactions. Our system
achieves a 93% and 87% task success rate on each of the two subtasks in
simulation and an 85% task success rate in real robot experiments on the whole
problem, which outperforms several baselines by large margins. Supplementary
material is available at https://sites.google.com/umn.edu/grasping-invisible.
