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Abstract:
Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and
the cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s,
however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the
perceptuomotor tasks of future robots. Instead, new hope was put in the growing wake of machine learning that promised fully
adaptive control algorithms which learn both by observation and trial-and-error. However, to date, learning techniques have yet
to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator and humanoid
robotics and usually scaling was only achieved in precisely pre-structured domains. We have investigated the ingredients for
a general approach to motor skill learning in order to get one step closer towards human-like performance. For doing so, we
study two major components for such an approach, i.e., firstly, a theoretically well-founded general approach to representing
the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can
be applied in this setting.
