  Existing learning from demonstration algorithms usually assume access to
expert demonstrations. However, this assumption is limiting in many real-world
applications since the collected demonstrations may be suboptimal or even
consist of failure cases. We therefore study the problem of learning from
imperfect demonstrations by learning a confidence predictor. Specifically, we
rely on demonstrations along with their confidence values from a different
correspondent environment (source environment) to learn a confidence predictor
for the environment we aim to learn a policy in (target environment -- where we
only have unlabeled demonstrations.) We learn a common latent space through
adversarial distribution matching of multi-length partial trajectories to
enable the transfer of confidence across source and target environments. The
learned confidence reweights the demonstrations to enable learning more from
informative demonstrations and discarding the irrelevant ones. Our experiments
in three simulated environments and a real robot reaching task demonstrate that
our approach learns a policy with the highest expected return.
