TeLoGraF：基于图编码流匹配的时序逻辑规划

TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching

May 1, 2025

作者: Yue Meng, Chuchu Fan

cs.AI

摘要

学习如何利用信号时序逻辑（STL）规范解决复杂任务，对众多现实世界应用至关重要。然而，由于缺乏多样化的STL数据集及有效提取时序逻辑信息以供下游任务使用的编码器，以往研究大多仅考虑固定或参数化的STL规范。本文提出TeLoGraF——时序逻辑图编码流，它结合图神经网络（GNN）编码器与流匹配技术，旨在学习适用于一般STL规范的解决方案。我们识别了四种常用的STL模板，并收集了总计20万条带有配对演示的规范。在从二维空间简单动力学模型到高维七自由度Franka Panda机械臂及Ant四足机器人导航的五个模拟环境中，我们进行了广泛实验。结果表明，在STL满足率方面，我们的方法优于其他基线。相较于经典的STL规划算法，我们的推理速度快10至100倍，且能适应任何系统动力学。此外，我们展示了图编码方法在解决复杂STL问题上的能力及其对分布外STL规范的鲁棒性。代码已发布于https://github.com/mengyuest/TeLoGraF。

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