Learning Inverse Dynamics: A Comparison

While it is well-known that model can enhance the control performance in terms of precision or energy eciency, the practical appli- cation has often been limited by the complexities of manually obtaining suciently accurate models. In the past, learning has proven a viable al- ternative 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? Tra- ditionally, locally weighted projection regression (LWPR), has been the standard method as it is capable of online, real-time learning for very com- plex robots. However, while LWPR has had signicant impact on learning in robotics, alternative nonparametric regression methods such as support vector regression (SVR) and Gaussian processes regression (GPR) oer interesting alternatives with fewer open parameters and potentially higher accuracy. In this paper, we evaluate these three alternatives for model learning. Our comparison consists out of the evaluation of learning qual- ity for each regression method using original data from SARCOS robot arm, as well as the robot tracking performance employing learned models. The results show that GPR and SVR achieve a superior learning precision and can be applied for real-time control obtaining higher accuracy. How- ever, for the online learning LWPR presents the better method due to its lower computational requirements.