  Control policies from imitation learning can often fail to generalize to
novel environments due to imperfect demonstrations or the inability of
imitation learning algorithms to accurately infer the expert's policies. In
this paper, we present rigorous generalization guarantees for imitation
learning by leveraging the Probably Approximately Correct (PAC)-Bayes framework
to provide upper bounds on the expected cost of policies in novel environments.
We propose a two-stage training method where a latent policy distribution is
first embedded with multi-modal expert behavior using a conditional variational
autoencoder, and then "fine-tuned" in new training environments to explicitly
optimize the generalization bound. We demonstrate strong generalization bounds
and their tightness relative to empirical performance in simulation for (i)
grasping diverse mugs, (ii) planar pushing with visual feedback, and (iii)
vision-based indoor navigation, as well as through hardware experiments for the
two manipulation tasks.
