Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing
Abstract
Diffusion models hold great potential in robotics due to their ability to capture complex, high-dimensional data distributions. However, their lack of constraint-awareness limits their deployment in safety-critical applications. We propose Constraint-Aware Diffusion Guidance (CoDiG), a data-efficient and general-purpose framework that integrates barrier functions into the denoising process, guiding diffusion sampling toward constraint-satisfying outputs. CoDiG enables constraint satisfaction even with limited training data and generalizes across tasks. We evaluate our framework in the challenging setting of miniature autonomous racing, where real-time obstacle avoidance is essential. Real-world experiments show that CoDiG generates safe outputs efficiently under dynamic conditions, highlighting its potential for broader robotic applications.
Cite
Text
Ma et al. "Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing." Proceedings of The 9th Conference on Robot Learning, 2025.Markdown
[Ma et al. "Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing." Proceedings of The 9th Conference on Robot Learning, 2025.](https://mlanthology.org/corl/2025/ma2025corl-constraintaware/)BibTeX
@inproceedings{ma2025corl-constraintaware,
title = {{Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing}},
author = {Ma, Hao and Bodmer, Sabrina and Carron, Andrea and Zeilinger, Melanie and Muehlebach, Michael},
booktitle = {Proceedings of The 9th Conference on Robot Learning},
year = {2025},
pages = {1756-1776},
volume = {305},
url = {https://mlanthology.org/corl/2025/ma2025corl-constraintaware/}
}