  Numerous past works have tackled the problem of task-driven navigation. But,
how to effectively explore a new environment to enable a variety of down-stream
tasks has received much less attention. In this work, we study how agents can
autonomously explore realistic and complex 3D environments without the context
of task-rewards. We propose a learning-based approach and investigate different
policy architectures, reward functions, and training paradigms. We find that
the use of policies with spatial memory that are bootstrapped with imitation
learning and finally finetuned with coverage rewards derived purely from
on-board sensors can be effective at exploring novel environments. We show that
our learned exploration policies can explore better than classical approaches
based on geometry alone and generic learning-based exploration techniques.
Finally, we also show how such task-agnostic exploration can be used for
down-stream tasks. Code and Videos are available at:
https://sites.google.com/view/exploration-for-nav.
