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From Animals to Animats 10

dc.contributor.authorBitzer, Sebastian
dc.contributor.authorHavoutis, Ioannis
dc.contributor.authorVijayakumar, Sethu
dc.contributor.editorAsada, Minoru
dc.contributor.editorHallam, John
dc.contributor.editorMeyer, Jean-Arcady
dc.contributor.editorTani, Jun
dc.date.accessioned2010-08-24T08:34:06Z
dc.date.available2010-08-24T08:34:06Z
dc.date.issued2008
dc.identifier.isbn978-3-540-69133-4en
dc.identifier.urihttp://www.springerlink.com/content/r4kq04513317w230/en
dc.identifier.urihttp://hdl.handle.net/1842/3667
dc.description.abstractWe propose a novel methodology for learning and synthesising whole classes of high dimensional movements from a limited set of demonstrated examples that satisfy some underlying ’latent’ low dimensional task constraints. We employ non-linear dimensionality reduction to extract a canonical latent space that captures some of the essential topology of the unobserved task space. In this latent space, we identify suitable parametrisation of movements with control policies such that they are easily modulated to generate novel movements from the same class and are robust to perturbations. We evaluate our method on controlled simulation experiments with simple robots (reaching and periodic movement tasks) as well as on a data set of very high-dimensional human (punching) movements.We verify that we can generate a continuum of new movements from the demonstrated class from only a few examples in both robotic and human data.en
dc.language.isoenen
dc.publisherSpringer-Verlagen
dc.relation.ispartofseriesInformatics Report Series
dc.relation.ispartofseriesEDI-INF-RR-1299
dc.subjectInformaticsen
dc.subjectComputer Scienceen
dc.titleSynthesising Novel Movements through Latent Space Modulation of Scalable Control Policiesen
dc.typeBook Chapteren
dc.identifier.doi10.1007/978-3-540-69134-1_20en
rps.titleFrom Animals to Animats 10en
dc.extent.pageNumbers10en
dc.date.updated2010-08-24T08:34:06Z


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