Paper on learning inverse statics models with symmetries published

Our recent work on learning inverse statics models efficiently has been published on arXiv by authors Rania Rayyes, Daniel Kubus, Carsten Hartmann and Jochen Steil. The paper describes an exploratory scheme to learn the inverse statics models online, from scratch and without using any controller. Moreover, it presents increasing learning efficiency by using Symmetries properties of the statics mapping. See also this Video