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

LAMP: Learning a Motion Policy to Repeatedly Navigate in an Uncertain Environment