Versatile Skill Control via Self-supervised Adversarial Imitation of
Unlabeled Mixed Motions
- URL: http://arxiv.org/abs/2209.07899v1
- Date: Fri, 16 Sep 2022 12:49:04 GMT
- Title: Versatile Skill Control via Self-supervised Adversarial Imitation of
Unlabeled Mixed Motions
- Authors: Chenhao Li, Sebastian Blaes, Pavel Kolev, Marin Vlastelica, Jonas
Frey, Georg Martius
- Abstract summary: We propose a cooperative adversarial method for obtaining versatile policies with controllable skill sets from unlabeled datasets.
We show that by utilizing unsupervised skill discovery in the generative imitation learning framework, novel and useful skills emerge with successful task fulfillment.
Finally, the obtained versatile policies are tested on an agile quadruped robot called Solo 8 and present faithful replications of diverse skills encoded in the demonstrations.
- Score: 19.626042478612572
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 utilizing
unsupervised skill discovery in the generative adversarial imitation learning
framework, novel and useful skills emerge with successful task fulfillment.
Finally, the obtained versatile policies are tested on an agile quadruped robot
called Solo 8 and present faithful replications of diverse skills encoded in
the demonstrations.
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