SaferPath: Hierarchical Visual Navigation with Learned Guidance and Safety-Constrained Control

TL;DR

SaferPath is a hierarchical visual navigation framework that combines learned guidance with safety-constrained optimization to improve success rates and reduce collisions in complex indoor environments.

Contribution

It introduces a novel hierarchical approach that refines learned guidance through safety-constrained optimization, enhancing robustness and safety in visual navigation.

Findings

Outperforms baselines like ViNT and NoMaD in success rates.

Reduces collision rates in dense and narrow environments.

Enables safe navigation in real-world challenging scenarios.

Abstract

Visual navigation is a core capability for mobile robots, yet end-to-end learning-based methods often struggle with generalization and safety in unseen, cluttered, or narrow environments. These limitations are especially pronounced in dense indoor settings, where collisions are likely and end-to-end models frequently fail. To address this, we propose SaferPath, a hierarchical visual navigation framework that leverages learned guidance from existing end-to-end models and refines it through a safety-constrained optimization-control module. SaferPath transforms visual observations into a traversable-area map and refines guidance trajectories using Model Predictive Stein Variational Evolution Strategy (MP-SVES), efficiently generating safe trajectories in only a few iterations. The refined trajectories are tracked by an MPC controller, ensuring robust navigation in complex environments. Extensive experiments in scenarios with unseen obstacles, dense unstructured spaces, and narrow corridors demonstrate that SaferPath consistently improves success rates and reduces collisions, outperforming representative baselines such as ViNT and NoMaD, and enabling safe navigation in challenging real-world settings.