  Mobile robots are often tasked with repeatedly navigating through an
environment whose traversability changes over time. These changes may exhibit
some hidden structure, which can be learned. Many studies consider reactive
algorithms for online planning, however, these algorithms do not take advantage
of the past executions of the navigation task for future tasks. In this paper,
we formalize the problem of minimizing the total expected cost to perform
multiple start-to-goal navigation tasks on a roadmap by introducing the Learned
Reactive Planning Problem. We propose a method that captures information from
past executions to learn a motion policy to handle obstacles that the robot has
seen before. We propose the LAMP framework, which integrates the generated
motion policy with an existing navigation stack. Finally, an extensive set of
experiments in simulated and real-world environments show that the proposed
method outperforms the state-of-the-art algorithms by 10% to 40% in terms of
expected time to travel from start to goal. We also evaluate the robustness of
the proposed method in the presence of localization and mapping errors on a
real robot.
