FeudalNav: A Simple Framework for Visual Navigation
Faith Johnson, Bryan Bo Cao, Shubham Jain, Ashwin Ashok, Kristin Dana

TL;DR
FeudalNav introduces a hierarchical, visual similarity-based memory framework for robotic navigation that performs competitively without odometry, and can be improved with minimal human interaction.
Contribution
This work presents a simple, transferable waypoint selection network and a visual similarity memory module, enabling effective navigation in unseen environments without odometry.
Findings
Competitive results with SOTA methods in Habitat AI environments.
The visual similarity memory suffices for navigation without explicit distance metrics.
Minimal human intervention can significantly improve navigation success.
Abstract
Visual navigation for robotics is inspired by the human ability to navigate environments using visual cues and memory, eliminating the need for detailed maps. In unseen, unmapped, or GPS-denied settings, traditional metric map-based methods fall short, prompting a shift toward learning-based approaches with minimal exploration. In this work, we develop a hierarchical framework that decomposes the navigation decision-making process into multiple levels. Our method learns to select subgoals through a simple, transferable waypoint selection network. A key component of the approach is a latent-space memory module organized solely by visual similarity, as a proxy for distance. This alternative to graph-based topological representations proves sufficient for navigation tasks, providing a compact, light-weight, simple-to-train navigator that can find its way to the goal in novel locations. We…
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