  Multimodal demonstrations provide robots with an abundance of information to
make sense of the world. However, such abundance may not always lead to good
performance when it comes to learning sensorimotor control policies from human
demonstrations. Extraneous data modalities can lead to state
over-specification, where the state contains modalities that are not only
useless for decision-making but also can change data distribution across
environments. State over-specification leads to issues such as the learned
policy not generalizing outside of the training data distribution. In this
work, we propose Masked Imitation Learning (MIL) to address state
over-specification by selectively using informative modalities. Specifically,
we design a masked policy network with a binary mask to block certain
modalities. We develop a bi-level optimization algorithm that learns this mask
to accurately filter over-specified modalities. We demonstrate empirically that
MIL outperforms baseline algorithms in simulated domains including MuJoCo and a
robot arm environment using the Robomimic dataset, and effectively recovers the
environment-invariant modalities on a multimodal dataset collected on a real
robot. Our project website presents supplemental details and videos of our
results at: https://tinyurl.com/masked-il
