Local navigation in cluttered environments often suffers from dense obstacles and frequent local minima. Conventional local planners rely on heuristics and are prone to failure, while deep reinforcement learning(DRL)based approaches provide adaptability but are constrained by limited onboard sensing. These limitations lead to navigation failures because the robot cannot perceive structures outside its field of view. In this paper, we propose DreamFlow, a DRL-based local navigation framework that extends the robot's perceptual horizon through conditional flow matching(CFM). The proposed CFM based prediction module learns probabilistic mapping between local height map latent representation and broader spatial representation conditioned on navigation context. This enables the navigation policy to predict unobserved environmental features and proactively avoid potential local minima. Experimental results demonstrate that DreamFlow outperforms existing methods in terms of latent prediction accuracy and navigation performance in simulation. The proposed method was further validated in cluttered real world environments with a quadrupedal robot. The project page is available at https://dreamflow-icra.github.io.

翻译：在杂乱环境中的局部导航常因密集障碍物和频繁的局部极小值而受阻。传统的局部规划器依赖启发式方法且易失效，而基于深度强化学习的方法虽具适应性，却受限于有限的机载感知能力。这些限制导致导航失败，因为机器人无法感知其视野之外的结构。本文提出DreamFlow，一种基于深度强化学习的局部导航框架，它通过条件流匹配扩展了机器人的感知范围。所提出的基于条件流匹配的预测模块，学习局部高度图潜在表示与更广阔空间表示之间的概率映射，该映射以导航上下文为条件。这使得导航策略能够预测未观测到的环境特征，并主动规避潜在的局部极小值。实验结果表明，在仿真环境中，DreamFlow在潜在预测精度和导航性能方面均优于现有方法。所提方法进一步在四足机器人于杂乱现实环境中的导航任务中得到了验证。项目页面详见 https://dreamflow-icra.github.io。