We are motivated by the problem of performing failure prediction for safety-critical robotic systems with high-dimensional sensor observations (e.g., vision). Given access to a black-box control policy (e.g., in the form of a neural network) and a dataset of training environments, we present an approach for synthesizing a failure predictor with guaranteed bounds on false-positive and false-negative errors. In order to achieve this, we utilize techniques from Probably Approximately Correct (PAC)-Bayes generalization theory. In addition, we present novel class-conditional bounds that allow us to trade-off the relative rates of false-positive vs. false-negative errors. We propose algorithms that train failure predictors (that take as input the history of sensor observations) by minimizing our theoretical error bounds. We demonstrate the resulting approach using extensive simulation and hardware experiments for vision-based navigation with a drone and grasping objects with a robotic manipulator equipped with a wrist-mounted RGB-D camera. These experiments illustrate the ability of our approach to (1) provide strong bounds on failure prediction error rates (that closely match empirical error rates), and (2) improve safety by predicting failures.

翻译：我们的动机是,对具有高维传感器观测(例如视觉)的安全临界机器人系统进行故障预测的问题。鉴于获得黑盒控制政策(例如以神经网络的形式)和训练环境的数据集,我们提出了一个方法,对故障预测进行合成,对假阳性和假阴性误差进行有保证的界限。为了实现这一点,我们使用了来自可能接近正确(PAC)-贝耶斯一般化理论的技术。此外,我们提出了新颖的等级条件界限,使我们能够交换假阳性相对于假阴性误差的相对比率。我们提出了一种算法,通过尽量减少我们的理论错误界限来训练失败预测器(作为传感器观测历史的投入)。我们用大规模模拟和硬件实验来展示由此产生的方法,用无人机进行基于视觉的导航,并用安装手腕升RGB-D相机的机器人操控器控制物体。这些实验表明,我们的方法能够(1) 提供故障预测误差率的严格界限(通过精确的实验误差率提高安全性),(通过精确的误差率提高安全性)。