  We present NavACL, a method of automatic curriculum learning tailored to the
navigation task. NavACL is simple to train and efficiently selects relevant
tasks using geometric features. In our experiments, deep reinforcement learning
agents trained using NavACL significantly outperform state-of-the-art agents
trained with uniform sampling -- the current standard. Furthermore, our agents
can navigate through unknown cluttered indoor environments to
semantically-specified targets using only RGB images. Obstacle-avoiding
policies and frozen feature networks support transfer to unseen real-world
environments, without any modification or retraining requirements. We evaluate
our policies in simulation, and in the real world on a ground robot and a
quadrotor drone. Videos of real-world results are available in the
supplementary material.
