用平均速度场生成路径，让机器人更快更准地找目标图。
RoamFlow: Reinforcement-Aligned One-Step Action MeanFlow Policy for Image-Goal Navigation
- 通过平均速度场直接预测行走方向，一步生成完整路径。
- 在仿真和真实机器人上均实现低延迟、高成功率导航。
- 两阶段训练：先模仿专家，再强化学习优化，效果稳定可靠。
图像目标导航是具身机器人中的核心挑战，即智能体需仅根据目标图像到达指定位置。现有强化学习方法直接将感知输入映射为动作，难以建模长时序依赖，常导致次优轨迹。为此，我们提出 RoamFlow，一种基于 MeanFlow 的生成式导航框架，通过预测平均速度场来合成轨迹，实现高效的少步生成并降低推理延迟。我们采用两阶段训练策略：先通过专家模仿实现稳定初始化，再通过强化学习进行任务特定策略优化。在 Habitat 模拟环境及真实机器人平台上的大量实验表明，RoamFlow 在满足实时性约束的同时保持了优异的导航性能。
原文摘要 · Abstract (English)
Image-goal navigation is a key challenge in embodied robotics, where an agent must reach a target specified solely by a goal image. While existing reinforcement learning approaches map perceptual observations directly to actions, they struggle to model long-horizon dependencies, often leading to suboptimal trajectories. To address this limitation, we propose RoamFlow, a generative navigation framework that leverages MeanFlow to predict the average velocity field for trajectory synthesis, enabling efficient few-step generation and reducing inference latency. We further adopt a two-stage training strategy that combines expert imitation for stable initialization with reinforcement learning for task-specific policy refinement. Extensive experiments in both Habitat simulation and real-world robotic platforms demonstrate that RoamFlow achieves efficient inference while maintaining strong navigation performance under real-time constraints.
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