Link Search Menu Expand Document

Generalization Guarantees for Imitation Learning

Paper PDF Code


Allen Ren (Princeton University)*; Sushant Veer (Princeton University); Anirudha Majumdar (Princeton University)

Interactive Session

2020-11-18, 12:30 - 13:00 PST | PheedLoop Session


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.


Reviews and Rebuttal

Reviews & Rebuttal

Conference on Robot Learning 2020