  We propose a learning-based navigation system for reaching visually indicated
goals and demonstrate this system on a real mobile robot platform. Learning
provides an appealing alternative to conventional methods for robotic
navigation: instead of reasoning about environments in terms of geometry and
maps, learning can enable a robot to learn about navigational affordances,
understand what types of obstacles are traversable (e.g., tall grass) or not
(e.g., walls), and generalize over patterns in the environment. However, unlike
conventional planning algorithms, it is harder to change the goal for a learned
policy during deployment. We propose a method for learning to navigate towards
a goal image of the desired destination. By combining a learned policy with a
topological graph constructed out of previously observed data, our system can
determine how to reach this visually indicated goal even in the presence of
variable appearance and lighting. Three key insights, waypoint proposal, graph
pruning and negative mining, enable our method to learn to navigate in
real-world environments using only offline data, a setting where prior methods
struggle. We instantiate our method on a real outdoor ground robot and show
that our system, which we call ViNG, outperforms previously-proposed methods
for goal-conditioned reinforcement learning, including other methods that
incorporate reinforcement learning and search. We also study how \sysName
generalizes to unseen environments and evaluate its ability to adapt to such an
environment with growing experience. Finally, we demonstrate ViNG on a number
of real-world applications, such as last-mile delivery and warehouse
inspection. We encourage the reader to visit the project website for videos of
our experiments and demonstrations sites.google.com/view/ving-robot.
