  Robot navigation is a task where reinforcement learning approaches are still
unable to compete with traditional path planning. State-of-the-art methods
differ in small ways, and do not all provide reproducible, openly available
implementations. This makes comparing methods a challenge. Recent research has
shown that unsupervised learning methods can scale impressively, and be
leveraged to solve difficult problems. In this work, we design ways in which
unsupervised learning can be used to assist reinforcement learning for robot
navigation. We train two end-to-end, and 18 unsupervised-learning-based
architectures, and compare them, along with existing approaches, in unseen test
cases. We demonstrate our approach working on a real life robot. Our results
show that unsupervised learning methods are competitive with end-to-end
methods. We also highlight the importance of various components such as input
representation, predictive unsupervised learning, and latent features. We make
all our models publicly available, as well as training and testing
environments, and tools. This release also includes OpenAI-gym-compatible
environments designed to emulate the training conditions described by other
papers, with as much fidelity as possible. Our hope is that this helps in
bringing together the field of RL for robot navigation, and allows meaningful
comparisons across state-of-the-art methods.
