TeLoGraF：基於圖編碼流匹配的時序邏輯規劃

TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching

May 1, 2025

作者: Yue Meng, Chuchu Fan

cs.AI

摘要

學習如何利用信號時序邏輯（STL）規範來解決複雜任務，對於許多現實世界的應用至關重要。然而，由於缺乏多樣化的STL數據集以及有效提取時序邏輯信息以供下游任務使用的編碼器，大多數先前的研究僅考慮固定或參數化的STL規範。在本論文中，我們提出了TeLoGraF，即時序邏輯圖編碼流，它利用圖神經網絡（GNN）編碼器和流匹配技術來學習通用STL規範的解決方案。我們識別了四種常用的STL模板，並收集了總計20萬條配對演示的規範。我們在五個模擬環境中進行了廣泛的實驗，範圍從二維空間中的簡單動力學模型到高維度的7自由度Franka Panda機械臂和Ant四足機器人導航。結果表明，我們的方法在STL滿足率上優於其他基線。與經典的STL規劃算法相比，我們的推理速度快了10到100倍，並且能夠適用於任何系統動力學。此外，我們展示了我們的圖編碼方法在解決複雜STL問題上的能力以及對分佈外STL規範的魯棒性。代碼可在https://github.com/mengyuest/TeLoGraF獲取。

English

Learning to solve complex tasks with signal temporal logic (STL) specifications is crucial to many real-world applications. However, most previous works only consider fixed or parametrized STL specifications due to the lack of a diverse STL dataset and encoders to effectively extract temporal logic information for downstream tasks. In this paper, we propose TeLoGraF, Temporal Logic Graph-encoded Flow, which utilizes Graph Neural Networks (GNN) encoder and flow-matching to learn solutions for general STL specifications. We identify four commonly used STL templates and collect a total of 200K specifications with paired demonstrations. We conduct extensive experiments in five simulation environments ranging from simple dynamical models in the 2D space to high-dimensional 7DoF Franka Panda robot arm and Ant quadruped navigation. Results show that our method outperforms other baselines in the STL satisfaction rate. Compared to classical STL planning algorithms, our approach is 10-100X faster in inference and can work on any system dynamics. Besides, we show our graph-encoding method's capability to solve complex STLs and robustness to out-distribution STL specifications. Code is available at https://github.com/mengyuest/TeLoGraF