TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching (2505.00562v1)

Abstract: 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: Temporal Logic Planning via Graph-encoded Flow Matching

In the paper titled "TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching," the authors present a novel method leveraging Graph Neural Networks (GNNs) combined with a flow-matching approach to improve signal temporal logic (STL) planning. STL is a robust framework used for specifying temporal and logical constraints in tasks critical to fields such as robotics and cyber-physical systems. The authors address the limitations of existing methods, which are often constrained to fixed or parametrized STL specifications, and introduce TeLoGraF to handle a broader scope of STL tasks.

Problem Context

Signal Temporal Logic is characterized by its ability to handle temporal dependencies and real-valued signals, making it versatile for expressing complex requirements, such as those involved in safety and efficiency in robotics. STL synthesis is recognized as NP-hard, which challenges classical methods like sampling-based, optimization-based, and gradient-based approaches due to their balancing acts between solution quality and computational efficiency. Existing learning-based methods have focused on handling a specified form of STL but lack generalizability to new STL specifications without retraining.

Contributions and Approach

The primary contributions of this paper include:

1. Generative Model for Diverse STL Specifications: TeLoGraF introduces a generative model that utilizes a GNN to encode STL specifications and a flow-matching mechanism to learn trajectories. This setup supports learning from diverse STL specifications, enabling adaptability without retraining.
2. Dataset Collection: The authors collect a substantial dataset comprising 200,000 diverse STL specifications, paired with demonstrations across five simulation environments. These include simple dynamical models and complex robotics systems, which provide a comprehensive dataset for training and evaluation.
3. Graph-based Encoding: A pivotal innovation is the use of GNNs to encode STL specifications as graph structures, allowing extraction of temporal logic information efficiently. This methodology harnesses the full representation capacity of syntax trees of STL formulas, enhancing the model's ability to process complex logical and temporal dependencies.
4. Performance Evaluation: TeLoGraF demonstrates significant improvements over classical and other learning-based methods in STL satisfaction rate and computational speed across multiple environments, including high-dimensional systems like the Franka Panda robot arm. The robustness of the graph-encoding approach to handle complex out-of-distribution STL specifications is underscored by performance on a diverse range of tasks.
5. Open-sourcing of Code and Dataset: The code and datasets are made publicly available to facilitate further research and development in STL planning.

Numerical Results and Implications

TeLoGraF notably outperformed baseline methods in terms of the STL satisfaction rate, achieving results that are 10 to 100 times faster than classical methods at inference. The ability to work across various system dynamics without sacrificing solution quality is a strong point of this approach.

Practically, these advancements imply better real-time decision-making capabilities for autonomous systems faced with temporal and logical constraints. Theoretically, this work suggests expansions in learning-based models' ability to generalize across complex logical frameworks. Improved adaptability and efficiency can foster developments in fields relying on stringent specification-driven design processes.

Future Directions

The results from TeLoGraF suggest promising avenues for advancing AI methodologies in handling temporal logic beyond the scope of current specifications, scaling to yet more complex environments and constraints. A key area of future development could focus on refining the model to handle more sophisticated STL constraints with increased robustness, potentially incorporating techniques from other areas such as reinforcement learning for dynamic environments.

In summary, the TeLoGraF approach has provided substantial improvements in STL planning, leveraging the expressive power of GNNs and efficient flow-matching. This work marks significant progress in computationally tractable solutions to NP-hard synthesis in signal temporal logic, opening up pathways for further research in AI-driven task planning.