Learning whom to trust in navigation: dynamically switching between classical and neural planning

TL;DR

This paper introduces a hierarchical navigation system that dynamically switches between classical and neural planners based on learned regularities, improving robot navigation performance in diverse environments.

Contribution

It proposes a novel high-level planner that learns to switch between classical and neural navigation policies, leveraging data-driven regularities for better performance.

Findings

Significant performance improvements in simulation and real-world tests.

Effective learning of scene-dependent failure patterns for planner switching.

Enhanced navigation robustness in complex environments.

Abstract

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.