  Grasp learning has become an exciting and important topic in robotics. Just a
few years ago, the problem of grasping novel objects from unstructured piles of
clutter was considered a serious research challenge. Now, it is a capability
that is quickly becoming incorporated into industrial supply chain automation.
How did that happen? What is the current state of the art in robotic grasp
learning, what are the different methodological approaches, and what machine
learning models are used? This review attempts to give an overview of the
current state of the art of grasp learning research.
