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.

翻译：在高维领域（如机器人学）中验证安全关键型自主系统是一项重大挑战。现有的基于马尔可夫链蒙特卡罗的黑盒方法可能需要海量样本，而基于重要性采样的方法通常依赖简单的参数族，可能难以表征故障分布。我们提出使用条件去噪扩散模型对故障分布进行采样，该模型已在机器人任务规划等复杂高维问题中展现出成功应用。我们通过迭代训练扩散模型以生成更接近故障的状态轨迹。在高维机器人验证任务上的实验表明，相较于现有黑盒技术，我们的方法在采样效率和模态覆盖方面均表现出显著优势。