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.

翻译：基于扩散模型的方法在数据驱动规划中展现出潜力，但缺乏安全保障，因此难以应用于安全关键领域。为解决这些挑战，我们提出了一种名为SafeDiffuser的新方法，通过采用一类控制障碍函数确保扩散概率模型满足规范。该方法的核心思想是将所提出的有限时间扩散不变性嵌入去噪扩散流程中，从而实现可信的扩散数据生成。此外，我们证明基于生成模型的有限时间扩散不变性方法不仅能保持泛化性能，还能在安全数据生成中增强鲁棒性。我们在迷宫路径生成、有腿机器人运动及三维空间操作等一系列安全规划任务上测试了该方法，结果表明其相对于原始扩散模型在鲁棒性和保证性方面具有优势。