让机器人导航更安全，通过精细调整模型理解障碍物边界。
SAFER-Nav: Enhancing Safety for Visual Robot Navigation via Segmentation-Aware Fine-Tuning
- 用分割感知微调让模型显式学习障碍物边界和可通行区域结构。
- 在多种环境与障碍场景中，碰撞率显著低于ViNT、NoMaD等基线方法。
- 兼容多种视觉主干网络，适合实际部署的机器人导航系统使用。
基于视觉的导航模型，尤其是基础模型，仅凭RGB观测即可生成可行轨迹。然而，即使最先进的基于Transformer和扩散模型的策略，在包含未见障碍物或条件变化的陌生环境中仍难以泛化，导致轨迹虽指向目标但不安全。现有方法通过外部轨迹修正或内部几何先验提升安全性，但这些策略未明确训练模型表征障碍物边界或可通行空间结构。为此，我们提出一种导航模型，通过微调将这些结构直接融入策略中，并兼容多种基于RGB的主干网络。在多个机器人平台、室内环境及静态与动态障碍物场景下，本方法相比ViNT、NoMaD及其CARE增强变体，显著降低了碰撞频率，同时保持了良好的到达目标性能。
原文摘要 · Abstract (English)
Vision-based navigation models, particularly foundation models, generate viable trajectories from RGB observations alone. However, even state-of-the-art transformer- and diffusion-based policies struggle to generalize in unfamiliar deployment environments containing unseen obstacles or shifted conditions. The resulting trajectories often remain goal-directed but unsafe. Existing efforts improve safety through external trajectory correction or internal geometric priors, yet the resulting policies are not trained to explicitly represent obstacle boundaries or traversable free-space structure. To address this, we propose a navigation model that incorporates these structures directly into the policy via fine-tuning and is designed to be compatible with diverse RGB-based backbones. Across multiple robot platforms, indoor environments, and static and dynamic obstacle scenarios, our method reduces collision frequency relative to ViNT, NoMaD, and their CARE-augmented variants while maintaining goal-reaching performance.
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