  Navigation of terrestrial robots is typically addressed either with
localization and mapping (SLAM) followed by classical planning on the
dynamically created maps, or by machine learning (ML), often through end-to-end
training with reinforcement learning (RL) or imitation learning (IL). Recently,
modular designs have achieved promising results, and hybrid algorithms that
combine ML with classical planning have been proposed. Existing methods
implement these combinations with hand-crafted functions, which cannot fully
exploit the complementary nature of the policies and the complex regularities
between scene structure and planning performance. Our work builds on the
hypothesis that the strengths and weaknesses of neural planners and classical
planners follow some regularities, which can be learned from training data, in
particular from interactions. This is grounded on the assumption that, both,
trained planners and the mapping algorithms underlying classical planning are
subject to failure cases depending on the semantics of the scene and that this
dependence is learnable: for instance, certain areas, objects or scene
structures can be reconstructed easier than others. We propose a hierarchical
method composed of a high-level planner dynamically switching between a
classical and a neural planner. We fully train all neural policies in
simulation and evaluate the method in both simulation and real experiments with
a LoCoBot robot, showing significant gains in performance, in particular in the
real environment. We also qualitatively conjecture on the nature of data
regularities exploited by the high-level planner.
