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Abstract

Robotics challenges can inspire and motivate new Machine Learning research as well as being an interest-
ing field of application of standard ML techniques. Despite the wide range of machine learning problems
encountered in robotics, the main bottleneck towards autonomous robots has been a lack of interaction
between the core robotics and the machine learning communities. To date, many roboticists still discard
machine learning approaches as generally inapplicable or inferior to classical, hand-crafted solutions. Simi-
larly, machine learning researchers do not yet acknowledge that robotics can play the same role for machine
learning which for instance physics had for mathematics: as a major application as well as a driving force
for new ideas, algorithms and approaches.
With the current rise of real, physical humanoid robots in robotics research labs around the globe, the need
for machine learning in robotics has grown significantly. Only if machine learning can succeed at making
robots fully adaptive, it is likely that we will be able to take real robots out of the research labs into real,
human inhabited environments. Among the important problems hidden in these steps are problems which
can be understood from the robotics and the machine learning point of view including perceptuo-action
coupling, imitation learning, movement decomposition, probabilistic planning problems, motor primitive
learning, reinforcement learning, model learning and motor control.