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Workshop on Robotics and Mathematics

dc.contributor.authorHoward, Matthewen
dc.contributor.authorVijayakumar, Sethuen
dc.date.accessioned2010-08-31T14:36:09Z
dc.date.available2010-08-31T14:36:09Z
dc.date.issued2007en
dc.identifier.other1200en
dc.identifier.urihttp://hdl.handle.net/1842/3709
dc.description.abstractWe consider the problem of direct policy learning in situations where the policies are only observable through their projections into the null-space of a set of dynamic, non-linear task constraints. We tackle the issue of deriving consistent data for the learning of such policies and make two contributions towards its solution. Firstly, we derive the conditions required to exactly reconstruct null-space policies and suggest a learning strategy based on this derivation. Secondly, we consider the case that the null-space policy is conservative and show that such a policy can be learnt more easily and robustly by learning the underlying potential function and using this as our representation of the policy.en
dc.relation.ispartofseriesInformatics Report Series
dc.relation.ispartofseriesEDI-INF-RR-1200
dc.titleReconstructing Null-space Policies Subject to Dynamic Task Constraints in Redundant Manipulatorsen
dc.typeConference Paperen
rps.titleWorkshop on Robotics and Mathematicsen
dc.date.updated2010-08-31T14:36:09Z


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