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.Comment: 19 pages, website: https://safediffuser.github.io/safediffuser

On the Forward Invariance of Neural ODEs

To ensure robust and trustworthy decision-making, it is highly desirable to enforce constraints over a neural network's parameters and its inputs automatically by back-propagating output specifications. This way, we can guarantee that the network makes reliable decisions under perturbations. Here, we propose a new method for achieving a class of specification guarantees for neural Ordinary Differentiable Equations (ODEs) by using invariance set propagation. An invariance of a neural ODE is defined as an output specification, such as to satisfy mathematical formulae, physical laws, and system safety. We use control barrier functions to specify the invariance of a neural ODE on the output layer and propagate it back to the input layer. Through the invariance backpropagation, we map output specifications onto constraints on the neural ODE parameters or its input. The satisfaction of the corresponding constraints implies the satisfaction of output specifications. This allows us to achieve output specification guarantees by changing the input or parameters while maximally preserving the model performance. We demonstrate the invariance propagation on a comprehensive series of representation learning tasks, including spiral curve regression, autoregressive modeling of joint physical dynamics, convexity portrait of a function, and safe neural control of collision avoidance for autonomous vehicles.Comment: 20 page

Telomerase prevents accelerated senescence in glucose-6-phosphate dehydrogenase (G6PD)-deficient human fibroblasts

Fibroblasts derived from glucose-6-phosphate dehydrogenase (G6PD)-deficient patients display retarded growth and accelerated cellular senescence that is attributable to increased accumulation of oxidative DNA damage and increased sensitivity to oxidant-induced senescence, but not to accelerated telomere attrition. Here, we show that ectopic expression of hTERT stimulates telomerase activity and prevents accelerated senescence in G6PD-deficient cells. Stable clones derived from hTERT-expressing normal and G6PD-deficient fibroblasts have normal karyotypes, and display no sign of senescence beyond 145 and 105 passages, respectively. Activation of telomerase, however, does not prevent telomere attrition in earlier-passage cells, but does stabilize telomere lengths at later passages. In addition, we provide evidence that ectopic expression of hTERT attenuates the increased sensitivity of G6PD-deficient fibroblasts to oxidant-induced senescence. These results suggest that ectopic expression of hTERT, in addition to acting in telomere length maintenance by activating telomerase, also functions in regulating senescence induction