ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models

• URL: http://arxiv.org/abs/2606.30696v1
• Date: Mon, 29 Jun 2026 02:22:31 GMT
• Title: ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models
• Abstract summary: We present ViTL, a framework that enables robots to follow natural language commands to complete zero-shot long-horizon tasks.<n>At the navigation level, we introduce directional score, rather than producing a direction-agnostic value across the entire field of view.<n>Experiments on Habitat-Matterport 3D show that the full framework enables zero-shot long-horizon completion of natural language navigation tasks.
• Score: 7.354500239997416
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Enabling robots to follow natural language commands to complete zero-shot long-horizon tasks remains challenging. It requires extracting implicit temporal and logical constraints from natural language commands and executing multiple sub-tasks accordingly. Recent zero-shot object navigation methods use vision-language models (VLMs) to guide frontier-based exploration in unknown environments, but they are limited to single-target tasks. Real-world commands such as "Clean either the chair or the couch, then turn on the tv." require navigating to multiple targets in a temporally constrained order, which no existing zero-shot system can handle. We present ViTL, a framework that addresses this gap at two levels. At the task level, we use a large language model (LLM) to compile natural language commands into Linear Temporal Logic (LTL) formulas, which are then converted into Deterministic Finite Automata~(DFA) that coordinate multi-channel value maps and trigger dynamic replanning when new objects are detected. At the navigation level, we introduce directional score: rather than producing a direction-agnostic value across the entire field of view, we label frontier directions on the observation image and extract per-direction scores from the VLM. Experiments on Habitat-Matterport 3D (HM3D) show that the full framework enables zero-shot long-horizon completion of natural language navigation tasks with temporal constraints, and that directional score improves single-target navigation accuracy and efficiency over the baseline.

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