SAFER-Nav: Enhancing Safety for Visual Robot Navigation via Segmentation-Aware Fine-Tuning
For robot navigation in unfamiliar environments, this method improves safety without sacrificing goal achievement, addressing a key limitation of current foundation models.
SAFER-Nav fine-tunes vision-based navigation policies to explicitly represent obstacle boundaries and free-space structure, reducing collision frequency by up to 50% compared to ViNT, NoMaD, and CARE-augmented variants while maintaining goal-reaching performance across multiple robot platforms and environments.
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.