DreamFlow: Local Navigation Beyond Observation via Conditional Flow Matching in the Latent Space
This addresses navigation failures for robots in dense, obstacle-rich settings by extending perceptual horizons, though it appears incremental as it builds on existing DRL and flow matching techniques.
DreamFlow tackles local navigation in cluttered environments by using conditional flow matching to predict unobserved features, enabling a robot to avoid local minima and achieve improved navigation performance in simulation and real-world tests.
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.