Effective robot navigation in dynamic environments is a challenging task that depends on generating precise control actions at high frequencies. Recent advancements have framed navigation as a goal-conditioned control problem. Current state-of-the-art methods for goal-based navigation, such as diffusion policies, either generate sub-goal images or robot control actions to guide robots. However, despite their high accuracy, these methods incur substantial computational costs, which limits their practicality for real-time applications. Recently, Conditional Flow Matching(CFM) has emerged as a more efficient and robust generalization of diffusion. In this work we explore the use of CFM to learn action policies that help the robot navigate its environment. Our results demonstrate that CFM is able to generate highly accurate robot actions. CFM not only matches the accuracy of diffusion policies but also significantly improves runtime performance. This makes it particularly advantageous for real-time robot navigation, where swift, reliable action generation is vital for collision avoidance and smooth operation. By leveraging CFM, we provide a pathway to more scalable, responsive robot navigation systems capable of handling the demands of dynamic and unpredictable environments.

翻译：动态环境中的有效机器人导航是一项具有挑战性的任务，其依赖于高频生成精确的控制动作。近期研究进展将导航问题构建为目标条件控制问题。当前基于目标的导航方法（例如扩散策略）通过生成子目标图像或机器人控制动作来引导机器人。然而，尽管这些方法具有较高的准确性，但其计算成本高昂，限制了它们在实时应用中的实用性。最近，条件流匹配作为一种更高效、更鲁棒的扩散模型泛化方法而出现。在本工作中，我们探索使用条件流匹配来学习帮助机器人在环境中导航的动作策略。我们的结果表明，条件流匹配能够生成高精度的机器人动作。它不仅能够匹配扩散策略的准确性，还显著提升了运行时性能。这使得它在实时机器人导航中尤其具有优势，因为快速可靠的动作生成对于避障和平稳运行至关重要。通过利用条件流匹配，我们为实现更具可扩展性、响应更快的机器人导航系统提供了一条途径，该系统能够满足动态和不可预测环境的需求。