  A key challenge in intelligent robotics is creating robots that are capable
of directly interacting with the world around them to achieve their goals. The
last decade has seen substantial growth in research on the problem of robot
manipulation, which aims to exploit the increasing availability of affordable
robot arms and grippers to create robots capable of directly interacting with
the world to achieve their goals. Learning will be central to such autonomous
systems, as the real world contains too much variation for a robot to expect to
have an accurate model of its environment, the objects in it, or the skills
required to manipulate them, in advance. We aim to survey a representative
subset of that research which uses machine learning for manipulation. We
describe a formalization of the robot manipulation learning problem that
synthesizes existing research into a single coherent framework and highlight
the many remaining research opportunities and challenges.
