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FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation
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FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation
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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.
Forward citations
Cited by 4 Pith papers
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EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild
EgoWalk supplies 50 hours of real-world multimodal human navigation data in varied indoor/outdoor settings together with open pipelines that auto-generate language goal annotations and traversability masks.
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NavCMPO: Critic-Guided MeanFlow Policy Optimization for Adaptive Navigation
A two-stage navigation policy using five-step MeanFlow generation, critic-guided trajectory refinement, and PPO fine-tuning reports higher success and lower latency than a matched NavDP baseline.
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RoamFlow: Reinforcement-Aligned One-Step Action MeanFlow Policy for Image-Goal Navigation
RoamFlow applies MeanFlow to predict average velocity fields for one-step action policies in image-goal navigation, trained via expert imitation followed by RL refinement.
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SEMNAV: Enhancing Visual Semantic Navigation in Robotics through Semantic Segmentation
SEMNAV trains visual semantic navigation policies on semantic segmentation inputs rather than RGB, reports higher success rates in Habitat 2.0 on HM3D, and shows improved real-world transfer on robotic platforms.
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