SafeDiffuser: Safe Planning with Diffusion Probabilistic Models

• URL: http://arxiv.org/abs/2306.00148v1
• Date: Wed, 31 May 2023 19:38:12 GMT
• Title: SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
• Authors: Wei Xiao and Tsun-Hsuan Wang and Chuang Gan and Daniela Rus
• Abstract summary: Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees. We propose a new method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy specifications. We test our method on a series of safe planning tasks, including maze path generation, legged robot locomotion, and 3D space manipulation.
• Score: 97.80042457099718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models [0.0]
  We show how diffusion-based models can be repurposed for performing principled, identifiable Bayesian inference. We show how such maps can be learned via standard DBM training using a novel noise schedule. The result is a class of highly expressive generative models, uniquely defined on a low-dimensional latent space.
  arXiv  Detail & Related papers  (2024-07-11T19:58:19Z)
• Watch the Watcher! Backdoor Attacks on Security-Enhancing Diffusion Models [65.30406788716104]
  This work investigates the vulnerabilities of security-enhancing diffusion models. We demonstrate that these models are highly susceptible to DIFF2, a simple yet effective backdoor attack. Case studies show that DIFF2 can significantly reduce both post-purification and certified accuracy across benchmark datasets and models.
  arXiv  Detail & Related papers  (2024-06-14T02:39:43Z)
• Predicting Cascading Failures with a Hyperparametric Diffusion Model [66.89499978864741]
  We study cascading failures in power grids through the lens of diffusion models. Our model integrates viral diffusion principles with physics-based concepts. We show that this diffusion model can be learned from traces of cascading failures.
  arXiv  Detail & Related papers  (2024-06-12T02:34:24Z)
• Distributionally Robust Safe Screening [14.973247943788234]
  We propose a Distributionally Robust Safe Screening (DRSS) method for identifying unnecessary samples and features. We provide a theoretical guarantee of the DRSS method and validate its performance through numerical experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-04-25T04:29:25Z)
• MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process [26.661721555671626]
  We introduce a novel Multi-Granularity Time Series (MG-TSD) model, which achieves state-of-the-art predictive performance. Our approach does not rely on additional external data, making it versatile and applicable across various domains.
  arXiv  Detail & Related papers  (2024-03-09T01:15:03Z)
• Fair Sampling in Diffusion Models through Switching Mechanism [5.560136885815622]
  We propose a fairness-aware sampling method called textitattribute switching mechanism for diffusion models. We mathematically prove and experimentally demonstrate the effectiveness of the proposed method on two key aspects.
  arXiv  Detail & Related papers  (2024-01-06T06:55:26Z)
• Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models [63.1637853118899]
  We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. We employ a shift control module that works on h-space of pre-trained diffusion models to manipulate a sample into a shifted version of itself. By jointly optimizing them, the model will spontaneously discover disentangled and interpretable directions.
  arXiv  Detail & Related papers  (2023-10-15T18:44:30Z)
• CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models [72.93652777646233]
  Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. We propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask.
  arXiv  Detail & Related papers  (2023-05-29T07:49:44Z)
• Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders [137.1060633388405]
  Diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. We propose a faster and cheaper approach that adds noise not until the data become pure random noise. We show that the proposed model can be cast as an adversarial auto-encoder empowered by both the diffusion process and a learnable implicit prior.
  arXiv  Detail & Related papers  (2022-02-19T20:18:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.