  Reinforcement learning can enable robots to navigate to distant goals while
optimizing user-specified reward functions, including preferences for following
lanes, staying on paved paths, or avoiding freshly mowed grass. However, online
learning from trial-and-error for real-world robots is logistically
challenging, and methods that instead can utilize existing datasets of robotic
navigation data could be significantly more scalable and enable broader
generalization. In this paper, we present ReViND, the first offline RL system
for robotic navigation that can leverage previously collected data to optimize
user-specified reward functions in the real-world. We evaluate our system for
off-road navigation without any additional data collection or fine-tuning, and
show that it can navigate to distant goals using only offline training from
this dataset, and exhibit behaviors that qualitatively differ based on the
user-specified reward function.
