  Physical modeling of robotic system behavior is the foundation for
controlling many robotic mechanisms to a satisfactory degree. Mechanisms are
also typically designed in a way that good model accuracy can be achieved with
relatively simple models and model identification strategies. If the modeling
accuracy using physically based models is not enough or too complex, model-free
methods based on machine learning techniques can help. Of particular interest
to us was therefore the question to what degree semi-parametric modeling
techniques, meaning combinations of physical models with machine learning,
increase the modeling accuracy of inverse dynamics models which are typically
used in robot control. To this end, we evaluated semi-parametric Gaussian
process regression and a novel model-based neural network architecture, and
compared their modeling accuracy to a series of naive semi-parametric,
parametric-only and non-parametric-only regression methods. The comparison has
been carried out on three test scenarios, one involving a real test-bed and two
involving simulated scenarios, with the most complex scenario targeting the
modeling a simulated robot's inverse dynamics model. We found that in all but
one case, semi-parametric Gaussian process regression yields the most accurate
models, also with little tuning required for the training procedure.
