  Recent success of machine learning in many domains has been overwhelming,
which often leads to false expectations regarding the capabilities of behavior
learning in robotics. In this survey, we analyze the current state of machine
learning for robotic behaviors. We will give a broad overview of behaviors that
have been learned and used on real robots. Our focus is on kinematically or
sensorially complex robots. That includes humanoid robots or parts of humanoid
robots, for example, legged robots or robotic arms. We will classify presented
behaviors according to various categories and we will draw conclusions about
what can be learned and what should be learned. Furthermore, we will give an
outlook on problems that are challenging today but might be solved by machine
learning in the future and argue that classical robotics and other approaches
from artificial intelligence should be integrated more with machine learning to
form complete, autonomous systems.
