Real-time safeguarding of diffusion policies without compromising task success

Establish whether real-time safeguarding of diffusion policies deployed in dynamic environments can be achieved without compromising task success; specifically, ascertain the existence and design of mechanisms that provide formal safety guarantees during deployment while preserving the learned task-completing behavior of diffusion-based visuomotor policies.

Background

Diffusion policies and related vision-language-action models have recently achieved strong performance on complex manipulation tasks by learning from large-scale demonstration datasets. However, they lack formal safety guarantees, and existing reactive safety mechanisms (e.g., control barrier functions) often push the system into out-of-distribution states, degrading task performance.

Prior approaches to safety for generative policies either guide the denoising process with costs or classifiers (which do not ensure hard constraints) or inject projections and barrier functions (which can change the policy and are computationally heavy, limiting real-time deployment or applicability to low-dimensional systems). Consequently, maintaining both real-time safety and task success for diffusion policies in dynamic environments remains unresolved.