title: TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
summary: 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
paper_authors._id: 68184c3fb727bc3cb7301e15
paper_authors.name: Yue Meng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:36:14.168Z
paper_authors._id: 668e100b97171f3399e07f5d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pgzatAZut-uoXOBOwgiFB.jpeg
paper_authors.fullname: Yue Meng
paper_authors.type: user
paper_authors.user: yuemithucsd
paper_authors._id: 68184c3fb727bc3cb7301e16
paper_authors.name: Chuchu Fan
paper_title: TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: Signal Temporal Logic (STL) TeLoGraF Graph Neural Networks (GNN) flow-matching STL satisfaction rate classical STL planning algorithms out-distribution STL specifications