Perception modules are integral in many modern autonomous systems, but their accuracy can be subject to the vagaries of the environment. In this paper, we propose a learning-based approach that can automatically characterize the error of a perception module from data and use this for safe control. The proposed approach constructs a {\em perception contract (PC)\/} which generates a set that contains the ground-truth value that is being estimated by the perception module, with high probability. We apply the proposed approach to study a vision pipeline deployed on a quadcopter. With the proposed approach, we successfully constructed a PC for the vision pipeline. We then designed a control algorithm that utilizes the learned PC, with the goal of landing the quadcopter safely on a landing pad. Experiments show that with the learned PC, the control algorithm safely landed the quadcopter despite the error from the perception module, while the baseline algorithm without using the learned PC failed to do so.

翻译：感知模块是现代许多自主系统中不可或缺的组成部分，但其准确性易受环境变化的影响。本文提出了一种基于学习的方法，能够自动从数据中描述感知模块的误差特征，并将其用于安全控制。该方法构建了一个“感知合约”（Perception Contract, PC），该合约以高概率生成一个包含感知模块所估计的真实值的集合。我们将所提方法应用于部署在四旋翼飞行器上的视觉处理管线，成功构建了该视觉管线的感知合约。随后，我们设计了一种利用所学习到感知合约的控制算法，旨在实现四旋翼飞行器安全着陆于降落平台。实验表明，尽管感知模块存在误差，采用所学习到的感知合约的控制算法仍能安全着陆，而未使用该感知合约的基线算法则未能实现安全着陆。