  Learning from demonstrations is an easy and intuitive way to show examples of
successful behavior to a robot. However, the fact that humans optimize or take
advantage of their body and not of the robot, usually called the embodiment
problem in robotics, often prevents industrial robots from executing the task
in a straightforward way. The shown movements often do not or cannot utilize
the degrees of freedom of the robot efficiently, and moreover suffer from
excessive execution errors. In this paper, we explore a variety of solutions
that address these shortcomings. In particular, we learn sparse movement
primitive parameters from several demonstrations of a successful table tennis
serve. The number of parameters learned using our procedure is independent of
the degrees of freedom of the robot. Moreover, they can be ranked according to
their importance in the regression task. Learning few parameters that are
ranked is a desirable feature to combat the curse of dimensionality in
Reinforcement Learning. Preliminary real robot experiments on the Barrett WAM
for a table tennis serve using the learned movement primitives show that the
representation can capture successfully the style of the movement with few
parameters.
