SAFER-Nav: 通过分割感知微调增强视觉机器人导航的安全性
SAFER-Nav: Enhancing Safety for Visual Robot Navigation via Segmentation-Aware Fine-Tuning
AI总结：
提出SAFER-Nav方法，通过分割感知微调将障碍物边界和可通行空间结构直接融入导航策略，降低碰撞频率并保持目标到达性能。
摘要：
基于视觉的导航模型，特别是基础模型，仅从RGB观测生成可行轨迹。然而，即使是最先进的基于Transformer和扩散的策略也难以在包含未见障碍物或条件变化的不熟悉部署环境中泛化。生成的轨迹通常仍以目标为导向但不安全。现有工作通过外部轨迹校正或内部几何先验提高安全性，但所得策略并未被训练显式表示障碍物边界或可通行自由空间结构。为解决此问题，我们提出一种导航模型，通过微调将这些结构直接纳入策略，并设计为与多种基于RGB的主干兼容。在多个机器人平台、室内环境以及静态和动态障碍物场景中，我们的方法相对于ViNT、NoMaD及其CARE增强变体降低了碰撞频率，同时保持目标到达性能。
英文摘要：
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 for transformer-based RGB navigation policies. Across three robot platforms, two indoor environments, and static and dynamic obstacle scenarios, our method reduces collisions per run from 1.76 to 0.20 and raises the goal arrival rate from 42% to 93% relative to ViNT, with consistent gains over NoMaD and their CARE-augmented variants. Project page: https://paper-demo.github.io/SAFER_Nav/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
