Rapid adaptation in unseen environments is essential for scalable real-world autonomy,
yet existing approaches rely on exhaustive exploration or rigid navigation policies that fail to generalize.
We present VLN-Zero, a two-phase vision-language navigation framework that leverages vision-language models
to efficiently construct symbolic scene graphs and enable zero-shot neurosymbolic navigation.
In the exploration phase, structured prompts guide VLM-based search toward informative and diverse trajectories,
yielding compact scene graph representations. In the deployment phase, a neurosymbolic planner reasons over
the scene graph and environmental observations to generate executable plans, while a cache-enabled
execution module accelerates adaptation by reusing previously computed task–location trajectories.
By combining rapid exploration, symbolic reasoning, and cache-enabled execution, the proposed framework overcomes the computational inefficiency and poor generalization of prior vision-language navigation methods, enabling robust and scalable decision-making in unseen environments. VLN-Zero achieves 2x higher success rate compared to state-of-the-art zero-shot models, outperforms most fine-tuned baselines, and reaches goal locations in half the time with 55% fewer VLM calls on average compared to state-of-the-art models across diverse environments.
