Show simple item record

IEEE International Conference on Robotics and Automation (ICRA '07)

dc.contributor.authorGeorgios Petkosen
dc.contributor.authorSethu Vijayakumaren
dc.date.accessioned2010-08-31T13:44:43Z
dc.date.available2010-08-31T13:44:43Z
dc.date.issued2007-04en
dc.identifier.issn1-4244-0601-3en
dc.identifier.other0983en
dc.identifier.urihttp://hdl.handle.net/1842/3696
dc.description.abstractRecent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it's use for robust predictive control. However, in realistic domains, system dynamics often change based on unobserved external contexts such as work load or contact conditions with other objects. Previous multiple model approaches to solving this problem are restricted to finite, discrete contexts without any generalization and have been tested only on linear systems. We present a framework for estimation of context through hidden latent variable extraction -- solely from experienced (non-linear) dynamics. This work refines the multiple model formalism to bootstrap context separation from context-unlabeled data and enables simultaneous online context estimation, dynamics learning and control based on a consistent probabilistic formulation. Most importantly, it extends the framework to a continuous latent model representation of context under specific assumptions of load distribution.en
dc.publisherIEEEen
dc.relation.ispartofseriesInformatics Report Series
dc.relation.ispartofseriesEDI-INF-RR-0983
dc.subjectLearning controlen
dc.titleContext Estimation and Learning Control through Latent Variable Extraction: From discrete to continuous contextsen
dc.typeConference Paperen
dc.identifier.doi10.1109/ROBOT.2007.363634en
rps.titleIEEE International Conference on Robotics and Automation (ICRA '07)en
dc.extent.pageNumbers2117-2123en
dc.date.updated2010-08-31T13:44:43Z


Files in this item

This item appears in the following Collection(s)

Show simple item record