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Proc. IEEE International Conference on Robotics and Automation (ICRA '09)

dc.contributor.authorHoward, Matthew
dc.contributor.authorKlanke, Stefan
dc.contributor.authorGienger, Michael
dc.contributor.authorGoerick, Christian
dc.contributor.authorVijayakumar, Sethu
dc.date.accessioned2010-08-23T10:10:28Z
dc.date.available2010-08-23T10:10:28Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/1842/3654
dc.description.abstractMany everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. As a key ingredient, we introduce a small but highly effective modification to the standard risk functional, allowing us to make a meaningful comparison between the estimated policy and constrained observations. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 27 degrees of freedom.en
dc.language.isoenen
dc.subjectInformaticsen
dc.subjectComputer Scienceen
dc.subjectRoboticsen
dc.titleA Novel Method for Learning Policies from Constrained Motionen
dc.typeConference Paperen
rps.titleProc. IEEE International Conference on Robotics and Automation (ICRA '09)en
dc.extent.noOfPages7en
dc.date.updated2010-08-23T10:10:28Z


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