arXiv:2603.25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations. Despite growing real-world deployment, existing evaluations rely almost exclusively on success rate, whether the robot reaches its goal, which conceals trajectory quality, collision behavior, and robustness to environmental change. We present a real-world evaluation of five state-of-the-art VNMs (GNM, ViNT, NoMaD, NaviBridger, and CrossFormer) across two robot platforms and five environments spanning indoor and out
The evaluation of Vision Foundation Models (VFMs) for real-world navigation is critical as these models move from laboratory settings to practical deployment, necessitating robust performance indicators beyond simple success rates.
This research provides crucial insights into the actual capabilities and limitations of cutting-edge Vision Navigation Models, directly impacting their commercial viability and safety in robot navigation applications.
The focus shifts from mere task completion (reaching a goal) to a more nuanced understanding of navigation quality, collision avoidance, and environmental robustness for robot autonomy.
- · Robot manufacturers
- · AI model developers specializing in robust navigation
- · Logistics and delivery sectors
- · Companies relying on simplistic robot navigation metrics
- · Developers of less robust VNM architectures
Improved Vision Navigation Models will lead to more reliable and safer autonomous robots in complex environments.
Enhanced robot navigation capabilities will accelerate the adoption of robotics in various industries, from logistics to service.
Widespread deployment of highly capable autonomous robots could lead to significant labor displacement and economic restructuring in sectors reliant on manual labor.
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Read at arXiv cs.LG