SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
Diffusion model-based approaches have shown promise in data-driven planning,
but there are no safety guarantees, thus making it hard to be applied for
safety-critical applications. To address these challenges, we propose a new
method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy
specifications by using a class of control barrier functions. The key idea of
our approach is to embed the proposed finite-time diffusion invariance into the
denoising diffusion procedure, which enables trustworthy diffusion data
generation. Moreover, we demonstrate that our finite-time diffusion invariance
method through generative models not only maintains generalization performance
but also creates robustness in safe data generation. We test our method on a
series of safe planning tasks, including maze path generation, legged robot
locomotion, and 3D space manipulation, with results showing the advantages of
robustness and guarantees over vanilla diffusion models.
647979dc0c2c2297c337e411
Wei Xiao
647979dc0c2c2297c337e412
Tsun-Hsuan Wang
647979dc0c2c2297c337e413
Chuang Gan
647979dc0c2c2297c337e414
Daniela Rus
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
Diffusion model-based approaches have shown promise in data-driven planning,
but there are no safety guarantees, thus making it hard to be applied for
safety-critical applications. To address these challenges, we propose a new
method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy
specifications by using a class of control barrier functions. The key idea of
our approach is to embed the proposed finite-time diffusion invariance into the
denoising diffusion procedure, which enables trustworthy diffusion data
generation. Moreover, we demonstrate that our finite-time diffusion invariance
method through generative models not only maintains generalization performance
but also creates robustness in safe data generation. We test our method on a
series of safe planning tasks, including maze path generation, legged robot
locomotion, and 3D space manipulation, with results showing the advantages of
robustness and guarantees over vanilla diffusion models.
SafeDiffuser ensures diffusion probabilistic models satisfy specifications using control barrier functions to create robust and safe data generation in planning tasks.
diffusion probabilistic models control barrier functions finite-time diffusion invariance denoising diffusion safe planning maze path generation legged robot locomotion 3D space manipulation