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.

我们提出一种新的方法SafeDiffuser，使用控制屏障函数来确保扩散概率模型满足规范，并将有限时间扩散不变性嵌入去噪扩散过程中，从而实现可信扩散数据生成，并证明我们的方法不仅具有一般化性能，而且还在安全数据生成中创建了鲁棒性。

SafeDiffuser: 弥散概率模型安全规划