用流匹配与深度先验提升机器人导航精度与速度
FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation
- 结合流匹配与现成模型的深度先验学习控制策略
- 实测导航准确率更高，推理速度显著更快
- 适合需高效实时导航的机器人研究者
在未知环境中实现高效的机器人导航是一项挑战性任务，要求在高频下精确执行控制动作。近年来，该问题被建模为图像目标条件控制问题，即机器人基于前向RGB图像生成导航动作。当前最先进方法采用扩散策略生成这些控制动作。尽管结果令人鼓舞，但这类模型计算开销大且感知能力弱。为此，我们提出FlowNav，一种新方法，利用现成基础模型提供的流匹配（CFM）与深度先验相结合，学习机器人导航的动作策略。实验表明，FlowNav在多个真实环境中的导航和探索任务中，显著优于现有最先进方法，在准确性与速度上均有明显提升。代码与训练模型已公开。
原文摘要 · Abstract (English)
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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