A Bayesian approach to empirical local linearization for robotics

— Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain the linearization, but in complex robotics systems where the dynamics and kinematics are often not faithfully obtainable, empirical linearization may be preferable. In this case, it is important to only use data for the local linearization that lies within a “reasonable” linear regime of the system, which can be defined from the Hessian at the point of the linearization— a quantity that is not available without an analytical model. We introduce a Bayesian approach to solve statistically what constitutes a “reasonable” local regime. We approach this problem in the context local linear regression. In contrast to previous locally linear methods, we avoid cross-validation or complex statistical hypothesis testing techniques to find the appropriate local regime. Instead, we treat the parameters of the local regime probabilistically and use approximat...

• Type: Conference
• Year: 2008
• Where: ICRA
• Authors: Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal
• Keywords: ICRA 2008, Linearization, Local Linearization, Local Regime, Robotics