FeudalNav: A Simple Framework for Visual Navigation

arXiv cs.RO / 4/27/2026

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Key Points

The paper introduces FeudalNav, a hierarchical learning framework for visual navigation that aims to work in GPS-denied or unmapped environments without relying on detailed metric maps.
It learns subgoal selection using a transferable waypoint-selection network and uses a latent-space memory module based on visual similarity instead of graph/topological representations.
The method is shown to navigate to goals in novel locations with a compact, lightweight design that is simple to train.
Experiments in Habitat AI environments report competitive results compared with state-of-the-art methods, while avoiding the use of odometry during both training and inference.
The framework also supports interactive navigation by quantifying how little human direction intervention is needed, showing that minimal human involvement can substantially improve success across trials.

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 show competitive results with a suite of SOTA methods in Habitat AI environments without using any odometry in training or inference. An additional contribution leverages the interpretablility of the framework for interactive navigation. We consider the question: how much direction intervention/interaction is needed to achieve success in all trials? We demonstrate that even minimal human involvement can significantly enhance overall navigation performance.