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Proc. 8th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

dc.contributor.authorHoward, Matthew
dc.contributor.authorKlanke, Stefan
dc.contributor.authorGienger, Michael
dc.contributor.authorGoerick, Christian
dc.contributor.authorVijayakumar, Sethu
dc.date.accessioned2010-08-23T10:59:48Z
dc.date.available2010-08-23T10:59:48Z
dc.date.issued2008
dc.identifier.issn978-1-4244-2821-2en
dc.identifier.urihttp://hdl.handle.net/1842/3658
dc.description.abstractWe present a method for learning potential-based policies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can combine observations from a variety of contexts where different constraints are in force, to learn the underlying unconstrained policy in form of its potential function. This allows us to generalise and predict behaviour where novel constraints apply. As a key ingredient, we first create multiple simple local models of the potential, and align those using an efficient algorithm.We can then detect and discard unsuitable subsets of the data and learn a global model from a cleanly pre-processed training set. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 22 degrees of freedom.en
dc.language.isoenen
dc.subjectInformaticsen
dc.subjectroboticsen
dc.titleLearning Potential-based Policies from Constrained Motionen
dc.typeConference Paperen
dc.identifier.doi10.1109/ICHR.2008.4755977en
rps.titleProc. 8th IEEE-RAS International Conference on Humanoid Robots (Humanoids)en
dc.extent.noOfPages7en
dc.date.updated2010-08-23T10:59:49Z


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