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

Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics