TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
Authors: Yue Meng, Chuchu Fan
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments in five simulation environments ranging from simple dynamical models in the 2D space to high-dimensional 7Do F Franka Panda robot arm and Ant quadruped navigation. Results show that our method outperforms other baselines in the STL satisfaction rate. |
| Researcher Affiliation | Academia | 1Department of Aeronautics and Astronautics, MIT, Cambridge, USA. Correspondence to: Yue Meng <EMAIL>. |
| Pseudocode | No | The paper describes its methodology in text and equations but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https: //github.com/mengyuest/Te Lo Gra F. |
| Open Datasets | Yes | all the code and the datasets will be open-sourced to promote the development of STL planning. |
| Dataset Splits | Yes | We use 80% for training and 20% for validation. |
| Hardware Specification | Yes | We use Nvidia L40S GPUs for the training, where each training job takes 6-24 hours on a single GPU. |
| Software Dependencies | No | The learning pipeline is implemented in Pytorch Geometric (Fey & Lenssen, 2019; Paszke et al., 2019). The loss function is constructed as: L = min(0.5 ρ(τ, 0, ϕ), 0) + c1 1 T t=0 {min(u2 t 1, 0) + min(v2 t 1, 0)} + c2 1 T t=0 (u2 t + v2 t ) (7) The first loss term maximizes the truncated robustness score ρ for the trajectory τ = (x0, u0, ..., u T 1, x T ) to ensure STL rule satisfaction for the STL ϕ. We use pytorch-kinematics (Zhong et al., 2024) library to leverage Pytorch and GPU devices to compute the forward kinematic in a parallelized and efficient way. While software packages like PyTorch, Pytorch Geometric, and pytorch-kinematics are mentioned, specific version numbers for these dependencies are not provided. |
| Experiment Setup | Yes | The training is conducted for 1000 epochs with a batch size of 256. We use the commonly used ADAM (Kingma, 2014) optimizer with an initial learning rate 5 10 4 and a cosine annealing schedule that reduces the learning rate to 5 10 5 at the 900-th epochs and then keep it as constant for the rest 100 epochs. In the flow matching, we set the flow step Ns = 100. |