title: SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
summary: 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.
paper_authors._id: 647979dc0c2c2297c337e411
paper_authors.name: Wei Xiao
paper_authors._id: 647979dc0c2c2297c337e412
paper_authors.name: Tsun-Hsuan Wang
paper_authors._id: 647979dc0c2c2297c337e413
paper_authors.name: Chuang Gan
paper_authors._id: 647979dc0c2c2297c337e414
paper_authors.name: Daniela Rus
paper_title: SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
paper_summary: 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.
paper_ai_summary: SafeDiffuser ensures diffusion probabilistic models satisfy specifications using control barrier functions to create robust and safe data generation in planning tasks.
paper_ai_keywords: diffusion probabilistic models control barrier functions finite-time diffusion invariance denoising diffusion safe planning maze path generation legged robot locomotion 3D space manipulation