Using model knowledge for learning inverse dynamics

— In recent years, learning models from data has become an increasingly interesting tool for robotics, as it allows straightforward and accurate model approximation. However, in most robot learning approaches, the model is learned from scratch disregarding all prior knowledge about the system. For many complex robot systems, available prior knowledge from advanced physics-based modeling techniques can entail valuable information for model learning that may result in faster learning speed, higher accuracy and better generalization. In this paper, we investigate how parametric physical models (e.g., obtained from rigid body dynamics) can be used to improve the learning performance, and, especially, how semiparametric regression methods can be applied in this context. We present two possible semiparametric regression approaches, where the knowledge of the physical model can either become part of the mean function or of the kernel in a nonparametric Gaussian process regression. We compar...

• Type: Journal
• Year: 2010
• Where: ICRA
• Authors: Duy Nguyen-Tuong, Jan Peters
• Keywords: ICRA 2010, Physical Model, Rigid Body Dynamics, Robotics, Semiparametric Regression