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Abstract

The number of advanced robot systems has been increasing in recent years yielding a
large variety of versatile designs with many degrees of freedom. These robots have the potential of
being applicable in uncertain tasks outside well-structured industrial settings. However, the complexity
of both systems and tasks is often beyond the reach of classical robot programming methods. As a
result, a more autonomous solution for robot task acquisition is needed where robots adaptively adjust
their behaviour to the encountered situations and required tasks.
Learning approaches pose one of the most appealing ways to achieve this goal. However, while
learning approaches are of high importance for robotics, we cannot simply use off-the-shelf methods
from the machine learning community as these usually do not scale into the domains of robotics due
to excessive computational cost as well as a lack of scalability. Instead, domain appropriate approaches
are needed. We focus here on several core domains of robot learning. For accurate task execution,
we need motor learning capabilities. For fast learning of the motor tasks, imitation learning offers the
most promising approach. Self improvement requires reinforcement learning approaches that scale into
the domain of complex robots. Finally, for efficient interaction of humans with robot systems, we will
need a form of interaction learning. This contribution provides a general introduction to these issues
and briefly presents the contributions of the related book chapters to the corresponding research topics.