  ObjectGoal Navigation (ObjectNav) is an embodied task wherein agents are to
navigate to an object instance in an unseen environment. Prior works have shown
that end-to-end ObjectNav agents that use vanilla visual and recurrent modules,
e.g. a CNN+RNN, perform poorly due to overfitting and sample inefficiency. This
has motivated current state-of-the-art methods to mix analytic and learned
components and operate on explicit spatial maps of the environment. We instead
re-enable a generic learned agent by adding auxiliary learning tasks and an
exploration reward. Our agents achieve 24.5% success and 8.1% SPL, a 37% and 8%
relative improvement over prior state-of-the-art, respectively, on the Habitat
ObjectNav Challenge. From our analysis, we propose that agents will act to
simplify their visual inputs so as to smooth their RNN dynamics, and that
auxiliary tasks reduce overfitting by minimizing effective RNN dimensionality;
i.e. a performant ObjectNav agent that must maintain coherent plans over long
horizons does so by learning smooth, low-dimensional recurrent dynamics. Site:
https://joel99.github.io/objectnav/
