  Walking controllers often require parametrization which must be tuned
according to some cost function. To estimate these parameters, simulations can
be performed which are cheap but do not fully represent reality. Real-robot
experiments, on the other hand, are more expensive and lead to hardware
wear-off. In this paper, we propose an approach for combining simulations and
real experiments to learn gait stabilization parameters. We use a Bayesian
optimization method which selects the most informative points in parameter
space to evaluate based on the entropy of the cost function to optimize.
Experiments with the igus Humanoid Open Platform demonstrate the effectiveness
of our approach.
