# SAFER-Nav: Enhancing Safety for Visual Robot Navigation via Segmentation-Aware Fine-Tuning Source: [SAFER-Nav: Enhancing Safety for Visual Robot Navigation via Segmentation-Aware Fine-Tuning]() Collection index: [llms.txt]() Data updated: 2026-06-12T05:00:05.419Z Authors: Geonyeong Ko, Giung Lee, Changjoo Nam Published: 2026-06-10T03:56:33.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Navigation on RoboMaster Environment TE](): Navigation; RoboMaster Environment TE; Goal Arrival Rate: 90. - [Navigation on RoboMaster Environment CY](): Navigation; RoboMaster Environment CY; Goal Arrival Rate: 90. - [Navigation on TurtleBot4 Environment TE](): Navigation; TurtleBot4 Environment TE; Goal Arrival Rate: 90. - [Navigation on TurtleBot4 Environment CY](): Navigation; TurtleBot4 Environment CY; Goal Arrival Rate: 90. - [Navigation on LoCoBot Environment TE](): Navigation; LoCoBot Environment TE; Goal Arrival Rate: 100. - [Navigation on LoCoBot Environment CY](): Navigation; LoCoBot Environment CY; Goal Arrival Rate: 100. - [Visual Navigation on Dynamic-obstacle scenario Corner-appear](): Visual Navigation; Dynamic-obstacle scenario Corner-appear; Collisions Count (Corner-appear): 0. - [Visual Navigation on Dynamic-obstacle scenario Front-approach](): Visual Navigation; Dynamic-obstacle scenario Front-approach; Collision Rate: 0. ## Original sources and implementation [Original paper]() [PDF]()