OpenFrontier: General Navigation with Visual-Language Grounded Frontiers

• URL: http://arxiv.org/abs/2603.05377v1
• Date: Thu, 05 Mar 2026 17:02:22 GMT
• Title: OpenFrontier: General Navigation with Visual-Language Grounded Frontiers
• Authors: Esteban Padilla, Boyang Sun, Marc Pollefeys, Hermann Blum,
• Abstract summary: Open-world navigation requires robots to make decisions in complex everyday environments.<n>Recent advances in vision--language navigation (VLN) and vision--language--action (VLA) models enable end-to-end policies conditioned on natural language.<n>We propose OpenFrontier, a training-free navigation framework that seamlessly integrates diverse vision--language prior models.
• Score: 54.661157616245966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-world navigation requires robots to make decisions in complex everyday environments while adapting to flexible task requirements. Conventional navigation approaches often rely on dense 3D reconstruction and hand-crafted goal metrics, which limits their generalization across tasks and environments. Recent advances in vision--language navigation (VLN) and vision--language--action (VLA) models enable end-to-end policies conditioned on natural language, but typically require interactive training, large-scale data collection, or task-specific fine-tuning with a mobile agent. We formulate navigation as a sparse subgoal identification and reaching problem and observe that providing visual anchoring targets for high-level semantic priors enables highly efficient goal-conditioned navigation. Based on this insight, we select navigation frontiers as semantic anchors and propose OpenFrontier, a training-free navigation framework that seamlessly integrates diverse vision--language prior models. OpenFrontier enables efficient navigation with a lightweight system design, without dense 3D mapping, policy training, or model fine-tuning. We evaluate OpenFrontier across multiple navigation benchmarks and demonstrate strong zero-shot performance, as well as effective real-world deployment on a mobile robot.

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