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

Georgios Petkos, Marc Toussaint, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2006
EditorsStefanos D. Kollias, Andreas Stafylopatis, Wlodzislaw Duch, Erkki Oja
PublisherSpringer-Verlag GmbH
Number of pages10
ISBN (Electronic)978-3-540-38627-8
ISBN (Print)978-3-540-38625-4
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg


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