  Validating safety-critical autonomous systems in high-dimensional domains
such as robotics presents a significant challenge. Existing black-box
approaches based on Markov chain Monte Carlo may require an enormous number of
samples, while methods based on importance sampling often rely on simple
parametric families that may struggle to represent the distribution over
failures. We propose to sample the distribution over failures using a
conditional denoising diffusion model, which has shown success in complex
high-dimensional problems such as robotic task planning. We iteratively train a
diffusion model to produce state trajectories closer to failure. We demonstrate
the effectiveness of our approach on high-dimensional robotic validation tasks,
improving sample efficiency and mode coverage compared to existing black-box
techniques.
