| dc.contributor.author | Holsten, Fredrik | en_US |
| dc.contributor.author | Darkner, Sune | en_US |
| dc.contributor.author | Engell-Nørregård, Morten P. | en_US |
| dc.contributor.author | Erleben, Kenny | en_US |
| dc.contributor.editor | Skouras, Melina | en_US |
| dc.date.accessioned | 2018-07-23T10:10:20Z | |
| dc.date.available | 2018-07-23T10:10:20Z | |
| dc.date.issued | 2018 | |
| dc.identifier.isbn | 978-3-03868-070-3 | |
| dc.identifier.issn | 1727-5288 | |
| dc.identifier.uri | https://doi.org/10.2312/sca.20181186 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/sca20181186 | |
| dc.description.abstract | Soft 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.publisher | The Eurographics Association | en_US |
| dc.subject | Computer systems organization | |
| dc.subject | Robotic control | |
| dc.subject | Computing methodologies | |
| dc.subject | Physical simulation | |
| dc.title | Local Models for Data Driven Inverse Kinematics of Soft Robots | en_US |
| dc.description.seriesinformation | Eurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters | |
| dc.description.sectionheaders | Posters | |
| dc.identifier.doi | 10.2312/sca.20181186 | |
| dc.identifier.pages | 7-8 | |
