STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models
Vision-language-action (VLA) models show impressive generalization but often lack interpretability and struggle with precise natural language instructions involving spatial, temporal, and logical constraints. This paper proposes a hierarchical framework using Signal Temporal Logic (STL) as a shared representation between high-level language understanding and low-level robot execution. The high-level policy uses a VLM to decompose instructions into subtasks, generates STL specifications, and selects low-level policies. STL constraints are enforced via model-predictive control or monitored during execution. Evaluated on a real-world tabletop domain, the framework improves precision, reliability, and interpretability of language-conditioned robot planning.
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[Submitted on 20 Jul 2026]
Title:STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models
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Abstract:Vision-language-action (VLA) models have shown impressive generalization, but often lack interpretability and can struggle to follow precise natural language instructions that encode spatial, temporal, and logical requirements. We propose a hierarchical framework that uses Signal Temporal Logic (STL) as a shared representation connecting high-level language understanding with low-level robot execution. A high-level policy leverages a VLM to decompose language instructions into high-level subtasks, generate STL specifications for each subtask, and choose a low-level policy for executing each subtask. The STL specifications translate language-derived intent into precise constraints, and the low-level policy selection determines whether those constraints are enforced directly through STL-guided model-predictive control or monitored during execution of a learned policy for perceptually complex, or contact-rich behaviors. By integrating STL into plan validation, low-level policy, subtask monitoring, and replanning, our framework enables language-derived plans to be checked, optimized, and revised at runtime using a common formal structure. We evaluate the approach on a real-world tabletop domain, demonstrating how formal specifications can improve the precision, reliability, and interpretability of language-conditioned robot planning.
Comments: 14 pages, 6 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.18580 [cs.RO]
(or arXiv:2607.18580v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.18580
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kasra Torshizi [view email] [v1] Mon, 20 Jul 2026 23:26:32 UTC (7,625 KB)
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