FlowNav: フローマッチングと深度事前分布を組み合わせた効率的なナビゲーション
FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation
フローマッチングと既存の基盤モデルから得られる深度事前分布を組み合わせて、ロボットナビゲーションの行動方策を学習する手法を提案し、従来法より高精度・高速なナビゲーションを実現した。
著者: Samiran Gode, Abhijeet Nayak, Débora N. P. Oliveira, Michael Krawez, Cordelia Schmid, Wolfram Burgard
分類: cs.RO
原文アブストラクト
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.
