让机器人提前‘看见’视野外障碍，主动避坑导航
DreamFlow: Local Navigation Beyond Observation via Conditional Flow Matching in the Latent Space
- 用条件流匹配预测视野外环境，扩展感知范围
- 在仿真中导航成功率超基线18%，误差降低37%
- 适合四足机器人在复杂真实场景自主移动
在密集障碍环境中，局部导航常因障碍物密集和局部极小值导致失败。传统规划器依赖启发式规则易失效，而深度强化学习方法受限于有限的本地传感，无法感知视域外结构。本文提出DreamFlow，一种基于DRL的局部导航框架，通过条件流匹配（CFM）扩展机器人感知范围。其基于CFM的预测模块，学习局部高程图潜在表示与更广阔空间表示之间的概率映射，条件于导航上下文，使导航策略能预测未观测环境特征并主动规避潜在局部极小值。实验表明，DreamFlow在仿真中显著提升潜在表示预测精度与导航性能。该方法进一步在四足机器人的真实复杂环境中验证，效果稳定。项目主页见 https://dreamflow-icra.github.io。
原文摘要 · Abstract (English)
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.
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