We present Safe Model Predictive Diffusion (Safe MPD), a training-free diffusion planner for generating provably safe and kinodynamically feasible trajectories. Our algorithm integrates a safety shield directly into the denoising process of a model-based diffusion framework. By enforcing feasibility and safety on every sample throughout the denoising process, our method avoids the common pitfalls of post-processing corrections, such as computational intractability and loss of feasibility. Through a parallelization in GPU, our method achieves sub-second planning times even on challenging, non-convex problems.
Motivation
Video
Acknowledgement
This work has been supported by Toyota Research Institute of North America (TRINA), Toyota Motor North America.
BibTex
@inproceedings{kim2026plcbf,
author = {Kim, Taekyung and Okamoto, Hideki and Hoxha, Bardh and Fainekos, Georgios and Panagou, Dimitra},
title = {Policy Library CBF: A Runtime Multimodal Safety Filter},
booktitle = {arXiv},
shorttitle = {PLCBF},
year = {2026}
}
LaTeX
복사