Web: http://arxiv.org/abs/2209.07899

Sept. 19, 2022, 1:11 a.m. | Chenhao Li, Sebastian Blaes, Pavel Kolev, Marin Vlastelica, Jonas Frey, Georg Martius

cs.LG updates on arXiv.org arxiv.org

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 single versatile policies with controllable skill sets
from unlabeled datasets containing diverse state transition patterns by
maximizing their discriminability. Moreover, we show that by …

arxiv mixed

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