  Learning agile skills is one of the main challenges in robotics. To this end,
reinforcement learning approaches have achieved impressive results. These
methods require explicit task information in terms of a reward function or an
expert that can be queried in simulation to provide a target control output,
which limits their applicability. In this work, we propose a generative
adversarial method for inferring reward functions from partial and potentially
physically incompatible demonstrations for successful skill acquirement where
reference or expert demonstrations are not easily accessible. Moreover, we show
that by using a Wasserstein GAN formulation and transitions from demonstrations
with rough and partial information as input, we are able to extract policies
that are robust and capable of imitating demonstrated behaviors. Finally, the
obtained skills such as a backflip are tested on an agile quadruped robot
called Solo 8 and present faithful replication of hand-held human
demonstrations.
