Learning Multiple Models of Non-linear Dynamics for Control Under Varying Contexts Export

@incollection{citeulike:2629235, abstract = {For stationary systems, efficient techniques for adaptive motor control exist which learn the system's inverse dynamics online and use this single model for control. However, in realistic domains the system dynamics often change depending on an external unobserved context, for instance the work load of the system or contact conditions with other objects. A solution to context-dependent control is to learn multiple inverse models for different contexts and to infer the current context by analyzing the experienced dynamics. Previous multiple model approaches have only been tested on linear systems. This paper presents an efficient multiple model approach for non-linear dynamics, which can bootstrap context separation from context-unlabeled data and realizes simultaneous online context estimation, control, and training of multiple inverse models. The approach formulates a consistent probabilistic model used to infer the unobserved context and uses Locally Weighted Projection Regression as an efficient online regressor which provides local confidence bounds estimates used for inference.}, author = {Petkos, Georgios and Toussaint, Marc and Vijayakumar, Sethu}, citeulike-article-id = {2629235}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11840817_93}, doi = {10.1007/11840817_93}, journal = {Artificial Neural Networks – ICANN 2006}, keywords = {function\_approximation, high\_dimensions}, pages = {898--907}, posted-at = {2008-04-04 11:25:39}, priority = {0}, title = {Learning Multiple Models of Non-linear Dynamics for Control Under Varying Contexts}, url = {http://dx.doi.org/10.1007/11840817_93}, year = {2006} }

TY - CHAP ID - citeulike:2629235 L3 - citeulike-article-id:2629235 N2 - For stationary systems, efficient techniques for adaptive motor control exist which learn the system’s inverse dynamics online and use this single model for control. However, in realistic domains the system dynamics often change depending on an external unobserved context, for instance the work load of the system or contact conditions with other objects. A solution to context-dependent control is to learn multiple inverse models for different contexts and to infer the current context by analyzing the experienced dynamics. Previous multiple model approaches have only been tested on linear systems. This paper presents an efficient multiple model approach for non-linear dynamics, which can bootstrap context separation from context-unlabeled data and realizes simultaneous online context estimation, control, and training of multiple inverse models. The approach formulates a consistent probabilistic model used to infer the unobserved context and uses Locally Weighted Projection Regression as an efficient online regressor which provides local confidence bounds estimates used for inference. JF - Artificial Neural Networks – ICANN 2006 EP - 907 TI - Learning Multiple Models of Non-linear Dynamics for Control Under Varying Contexts SP - 898 KW - function_approximation KW - high_dimensions AU - Petkos, Georgios AU - Toussaint, Marc AU - Vijayakumar, Sethu PY - 2006/// UR - http://dx.doi.org/10.1007/11840817_93 ER -