Holsten, FredrikDarkner, SuneEngell-Nørregård, Morten P.Erleben, KennySkouras, Melina2018-07-232018-07-232018978-3-03868-070-31727-5288https://doi.org/10.2312/sca.20181186https://diglib.eg.org:443/handle/10.2312/sca20181186Soft 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.Computer systems organizationRobotic controlComputing methodologiesPhysical simulation10.2312/sca.201811867-8