中文标题#
FlowNav：結合流匹配和深度先驗的高效導航
英文标题#
FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation
中文摘要#
在未見過的環境中實現有效的機器人導航是一項具有挑戰性的任務，這需要在高頻下進行精確的控制動作。 最近的進展將其視為一個圖像目標條件控制問題，其中機器人使用正面 RGB 圖像生成導航動作。 該領域的最新方法使用擴散策略來生成這些控制動作。 儘管它們的結果很有前景，但這些模型計算成本高且感知能力弱。 為了解決這些限制，我們提出了 FlowNav，這是一種新方法，它結合了來自現成基礎模型的 CFM 和深度先驗來學習機器人導航的動作策略。 FlowNav 在導航和探索方面比最新方法更準確、更快。 我們通過在多個環境中的真實機器人實驗驗證了我們的貢獻，展示了改進的導航可靠性和準確性。 代碼和訓練好的模型是公開的。
英文摘要#
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.
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