Learning Inverse Dynamics: a Comparison

While it is well-known that model can enhance the control performance in terms of precision or energy efficiency, the practical application has often been limited by the complexities of manually obtaining sufficiently accurate models. In the past, learning has proven a viable alternative to using a combination of rigid-body dynamics and handcrafted approximations of nonlinearities. However, a major open question is what nonparametric learning method is suited best for learning dynamics? Traditionally, locally weighted projection regression (LWPR), has been the standard method as it is capable of online, real-time learning for very complex robots. However, while LWPR has had significant impact on learning in robotics, alternative nonparametric regression methods such as support vector regression (SVR) and Gaussian processes regression (GPR) offer interesting alternatives with fewer open parameters and potentially higher accuracy. In this paper, we evaluate these three alternatives for m...

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Duy Nguyen-Tuong, Jan Peters, Matthias Seeger, Bernhard Schölkopf
• Keywords: Alternative Nonparametric Regression, ESANN 2008, Neural Networks, Nonparametric Learning Method, Regression Method