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 article, 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.

The ability to navigate uncertain environments from a start to a goal location is a necessity in many applications. While there are many reactive algorithms for online replanning, there has not been much investigation in leveraging past executions of the same navigation task to improve future executions. In this work, we first formalize this problem by introducing the Learned Reactive Planning Problem (LRPP). Second, we propose a method to capture these past executions and from that determine a motion policy to handle obstacles that the robot has seen before. Third, we show from our experiments that using this policy can significantly reduce the execution cost over just using reactive algorithms.