Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning

This repository contains the code for our CDC2026 submission: Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning (Dechuan Liu, Ruigang Wang, and Ian R. Manchester).

This code has been tested with Python 3.12.3.

A few results

We learn a bi-Lipschitz diffeomorphism g to encode the geometry of obstacles.

We construal goal-conditioned neural ODE, which induces the safe, stable, all-pairs motion planning with formal guarantees.

Installation and Setup

Please also install the packages listed in requirements.txt

Organisation of this Repository

This repositroy is structured as follows.

src/: contains all source code used to run experiments, process results, and generate plots.

results/: contains all plots and saved model weights used to produce the main results figures in the paper.

Usage

Run following script to generate the dataset by RRT:

python src/dataset_generation.py 

Run following script to train and save the models:

python src/train_model.py

Run following script to visualize the results:

python src/visualization.py

Contact

For any questions or bugs, please raise an issue or contact Ruigang (Ray) Wang (ruigang.wang@sydney.edu.au) or Dechuan Liu (dechuan.liu@sydney.edu.au)