  We describe a robotic learning system for autonomous exploration and
navigation in diverse, open-world environments. At the core of our method is a
learned latent variable model of distances and actions, along with a
non-parametric topological memory of images. We use an information bottleneck
to regularize the learned policy, giving us (i) a compact visual representation
of goals, (ii) improved generalization capabilities, and (iii) a mechanism for
sampling feasible goals for exploration. Trained on a large offline dataset of
prior experience, the model acquires a representation of visual goals that is
robust to task-irrelevant distractors. We demonstrate our method on a mobile
ground robot in open-world exploration scenarios. Given an image of a goal that
is up to 80 meters away, our method leverages its representation to explore and
discover the goal in under 20 minutes, even amidst previously-unseen obstacles
and weather conditions. Please check out the project website for videos of our
experiments and information about the real-world dataset used at
https://sites.google.com/view/recon-robot.
