Estimating the distribution over failures is a key step in validating autonomous systems. Existing approaches focus on finding failures for a small range of initial conditions or make restrictive assumptions about the properties of the system under test. We frame estimating the distribution over failure trajectories for sequential systems as Bayesian inference. Our model-based approach represents the distribution over failure trajectories using rollouts of system dynamics and computes trajectory gradients using automatic differentiation. Our approach is demonstrated in an inverted pendulum control system, an autonomous vehicle driving scenario, and a partially observable lunar lander. Sampling is performed using an off-the-shelf implementation of Hamiltonian Monte Carlo with multiple chains to capture multimodality and gradient smoothing for safe trajectories. In all experiments, we observed improvements in sample efficiency and parameter space coverage compared to black-box baseline approaches. This work is open sourced.

翻译：估计失效轨迹的分布是验证自主系统的关键步骤。现有方法通常仅针对小范围初始条件寻找失效情况，或对待测系统的性质做出严格假设。本文将序列系统中失效轨迹分布的估计问题转化为贝叶斯推断问题。我们提出的基于模型方法利用系统动力学推演来表征失效轨迹分布，并通过自动微分计算轨迹梯度。该方法在倒立摆控制系统、自动驾驶车辆行驶场景以及部分可观测的月球着陆器任务中进行了验证。采样过程采用现成的哈密顿蒙特卡洛实现，通过多链采样捕捉多模态特性，并对安全轨迹进行梯度平滑处理。在所有实验中，我们观察到与黑箱基线方法相比，样本效率与参数空间覆盖率均有所提升。本项工作已开源。