TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
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
68184c3fb727bc3cb7301e15
Yue Meng
admin_assigned
2025-05-05T07:36:14.168Z
668e100b97171f3399e07f5d
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Yue Meng
user
yuemithucsd
68184c3fb727bc3cb7301e16
Chuchu Fan
TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
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
TeLoGraF uses Graph Neural Networks and flow-matching to solve general STL specifications in various simulation environments, outperforming baselines in STL satisfaction rate and inference speed.
Signal Temporal Logic (STL) TeLoGraF Graph Neural Networks (GNN) flow-matching STL satisfaction rate classical STL planning algorithms out-distribution STL specifications