  Behavioral cloning has proven to be effective for learning sequential
decision-making policies from expert demonstrations. However, behavioral
cloning often suffers from the causal confusion problem where a policy relies
on the noticeable effect of expert actions due to the strong correlation but
not the cause we desire. This paper presents Object-aware REgularizatiOn
(OREO), a simple technique that regularizes an imitation policy in an
object-aware manner. Our main idea is to encourage a policy to uniformly attend
to all semantic objects, in order to prevent the policy from exploiting
nuisance variables strongly correlated with expert actions. To this end, we
introduce a two-stage approach: (a) we extract semantic objects from images by
utilizing discrete codes from a vector-quantized variational autoencoder, and
(b) we randomly drop the units that share the same discrete code together,
i.e., masking out semantic objects. Our experiments demonstrate that OREO
significantly improves the performance of behavioral cloning, outperforming
various other regularization and causality-based methods on a variety of Atari
environments and a self-driving CARLA environment. We also show that our method
even outperforms inverse reinforcement learning methods trained with a
considerable amount of environment interaction.
