holsten.darkner.ea18
Best Poster Prize: Authors
Fredrik Holsten, Sune Darkner, Morten P. Engell-Nørregård, and Kenny Erleben

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.

SCA 2018 Meta Data download

SCA Poster Abstract download

Get Open Source code

Advertisements