STeP: 信号時相論理による視覚言語モデルを用いた行動生成の精密仕様化
STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models
視覚言語行動モデルによる指示分解を、信号時相論理(STL)で精密な制約に変換し、実行時検証・監視・再計画を行う階層的フレームワークを提案した。
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著者: Kasra Torshizi, Anukriti Singh, Sidharth Mathur, Khuzema Habib, Leo Du, Pratap Tokekar
分類: cs.RO
原文アブストラクト
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.
