ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models
ViTL framework uses LLMs to compile natural language commands into Linear Temporal Logic formulas, which are converted into Deterministic Finite Automata to coordinate multi-channel value maps. It introduces a directional score for navigation, enabling zero-shot completion of multi-target, temporally constrained navigation tasks. Experiments on HM3D demonstrate its effectiveness.
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[Submitted on 29 Jun 2026]
Title:ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models
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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.
Subjects:
Robotics (cs.RO); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2606.30696 [cs.RO]
(or arXiv:2606.30696v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.30696
arXiv-issued DOI via DataCite
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From: Kaier Liang [view email] [v1] Mon, 29 Jun 2026 02:22:31 UTC (1,446 KB)
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