SafeDiffuser: Safe Planning with Diffusion Probabilistic Models

Wei Xiao, Tsun-Hsuan Wang, Chuang Gan, Ramin Hasani, Mathias Lechner, Daniela Rus

Keywords: Diffusion, Safe Planning, Specification Guarantees

TL;DR: We propose a new method to ensure diffusion probabilistic models satisfy specifications by using a class of control barrier functions

Abstract: Diffusion model-based approaches have shown promise in data-driven planning. Although these planners are typically used in decision-critical applications, there are yet no known safety guarantees established for them. In this paper, we address this limitation by introducing SafeDiffuser, a method to equip probabilistic diffusion models with safety guarantees via control barrier functions. The key idea of our approach is to embed finite-time diffusion invariance, i.e., a form of specification mainly consisting of safety constraints, into the denoising diffusion procedure. This way we enable data generation under safety constraints. We show that SafeDiffusers maintain the generative performance of diffusion models while also providing robustness in safe data generation. We finally test our method on a series of planning tasks, including maze path generation, legged robot locomotion, and 3D space manipulation, and demonstrate the advantages of robustness over vanilla diffusion models.

Primary Area: generative models

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 8525