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dc.contributor.authorHolsten, Fredriken_US
dc.contributor.authorDarkner, Suneen_US
dc.contributor.authorEngell-Nørregård, Morten P.en_US
dc.contributor.authorErleben, Kennyen_US
dc.contributor.editorSkouras, Melinaen_US
dc.date.accessioned2018-07-10T18:01:01Z
dc.date.available2018-07-10T18:01:01Z
dc.date.issued2018
dc.identifier.isbn978-3-03868-070-3
dc.identifier.issn1727-5288
dc.identifier.urihttps://doi.org/10.2312/sca.20181186
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/sca20181186
dc.description.abstractSoft robots are attractive because they have the potential of being safer, faster and cheaper than traditional rigid robots. If we can predict the shape of a soft robot for a given set of control parameters, then we can solve the inverse problem: to find an optimal set of control parameters for a given shape. This work takes a data-driven approach to create multiple local inverse models. This has two benefits: (1) We overcome the reality gap and (2) we gain performance and naive parallelism from using local models. Furthermore, we empirically prove that our approach outperforms a higher order global model.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputer systems organization
dc.subjectRobotic control
dc.subjectComputing methodologies
dc.subjectPhysical simulation
dc.titleLocal Models for Data Driven Inverse Kinematics of Soft Robotsen_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters
dc.description.sectionheadersPosters
dc.identifier.doi10.2312/sca.20181186
dc.identifier.pages7-8


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