DreamFlow: Local Navigation Beyond Observation via Conditional Flow Matching in the Latent Space

2026-03-03

AI Summary

This work proposes DreamFlow, a novel framework that addresses the challenge of local navigation in densely cluttered environments, where limited perceptual range often leads to entrapment in local minima. For the first time, conditional flow matching (CFM) is introduced into local navigation to learn a probabilistic mapping between local elevation maps and global spatial representations. By predicting the structure of unobserved regions in latent space, DreamFlow enables proactive avoidance of potential traps, overcoming the limitations of conventional strategies that rely solely on immediate observations. Experimental results demonstrate that DreamFlow significantly improves latent-space prediction accuracy and navigation success rates in simulation, while real-world evaluations on quadrupedal robots confirm its robustness and efficiency in complex, cluttered scenarios.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

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.

Problem

• local navigation
• cluttered environments
• limited sensing
• local minima
• perceptual horizon

Innovation

• Conditional Flow Matching
• Latent Space Prediction
• Local Navigation
• Deep Reinforcement Learning
• Perceptual Horizon Extension