IMOL paper on explorative learning of torque models accepted

Our contribution on Learning Gravity Compensation with Goal Babbling by authors Rania Rayyes, Daniel Kubus, and Jochen Steil was accepted and will be presented at the IMOL 2017 - The Third International Workshop on Intrinsically Motivated Open-ended Learning (http://www.imol-conf.org/). The paper describes an exploratory scheme to learn the static forces needed to compensate gravitation for several robotic models.