FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation

TL;DR

FlowNav introduces a novel navigation approach that combines flow matching and depth priors to improve accuracy and speed in unseen environments, addressing computational and perception limitations of existing methods.

Contribution

FlowNav is the first to integrate flow matching with depth priors from foundation models for efficient robot navigation in unseen environments.

Findings

FlowNav outperforms state-of-the-art methods in accuracy and speed.

FlowNav demonstrates improved navigation reliability in real robot experiments.

FlowNav is computationally more efficient than diffusion policy-based approaches.

Abstract

Effective robot navigation in unseen environments is a challenging task that requires precise control actions at high frequencies. Recent advances have framed it as an image-goal-conditioned control problem, where the robot generates navigation actions using frontal RGB images. Current state-of-the-art methods in this area use diffusion policies to generate these control actions. Despite their promising results, these models are computationally expensive and suffer from weak perception. To address these limitations, we present FlowNav, a novel approach that uses a combination of CFM and depth priors from off-the-shelf foundation models to learn action policies for robot navigation. FlowNav is significantly more accurate and faster at navigation and exploration than state-of-the-art methods. We validate our contributions using real robot experiments in multiple environments, demonstrating improved navigation reliability and accuracy. Code and trained models are publicly available.