  While recent model-free Reinforcement Learning (RL) methods have demonstrated
human-level effectiveness in gaming environments, their success in everyday
tasks like visual navigation has been limited, particularly under significant
appearance variations. This limitation arises from (i) poor sample efficiency
and (ii) over-fitting to training scenarios. To address these challenges, we
present a world model that learns invariant features using (i) contrastive
unsupervised learning and (ii) an intervention-invariant regularizer. Learning
an explicit representation of the world dynamics i.e. a world model, improves
sample efficiency while contrastive learning implicitly enforces learning of
invariant features, which improves generalization. However, the na\"ive
integration of contrastive loss to world models is not good enough, as
world-model-based RL methods independently optimize representation learning and
agent policy. To overcome this issue, we propose an intervention-invariant
regularizer in the form of an auxiliary task such as depth prediction, image
denoising, image segmentation, etc., that explicitly enforces invariance to
style interventions. Our method outperforms current state-of-the-art
model-based and model-free RL methods and significantly improves on
out-of-distribution point navigation tasks evaluated on the iGibson benchmark.
With only visual observations, we further demonstrate that our approach
outperforms recent language-guided foundation models for point navigation,
which is essential for deployment on robots with limited computation
capabilities. Finally, we demonstrate that our proposed model excels at the
sim-to-real transfer of its perception module on the Gibson benchmark.
