Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions
Chenhao Li,Sebastian Blaes,Pavel Kolev,Marin Vlastelica,Jonas Frey,Georg Martius,Chenhao Li,Sebastian Blaes,Pavel Kolev,Marin Vlastelica,Jonas Frey,Georg Martius
Learning diverse skills is one of the main challenges in robotics. To this end, imitation learning approaches have achieved impressive results. These methods require explicitly labeled datasets or assume consistent skill execution to enable learning and active control of individual behaviors, which limits their applicability. In this work, we propose a cooperative adversarial method for obtaining ...
