Balance learned and classical modules in navigation systems

Establish principled frameworks to combine learned components and classical modules (e.g., mapping, localization, planning) in visual navigation systems so as to retain the generalization and explainability of classical stacks while leveraging the performance of end‑to‑end reinforcement learning.

Background

Navigation research spans end‑to‑end learning approaches and modular pipelines that integrate mapping and planning. While end‑to‑end RL excels in simulation, modular designs often offer better generalization and interpretability in the real world.

The survey emphasizes that determining the proper integration boundaries and roles of learned versus classical components is still unresolved and central to building robust, deployable navigation systems.