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 an inverse perception contract (IPC) 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 an IPC for the vision pipeline. We then designed a control algorithm that utilizes the learned IPC, with the goal of landing the quadcopter safely on a landing pad. Experiments show that with the learned IPC, the control algorithm safely landed the quadcopter despite the error from the perception module, while the baseline algorithm without using the learned IPC failed to do so.

翻译：感知模块是现代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影响。本文提出一种基于学习的方法，能够从数据中自动表征感知模块的误差特性，并将其用于安全控制。该方法构建了逆向感知契约（IPC），该契约以高概率生成包含感知模块所估计真实值的集合。我们将所提方法应用于四旋翼飞行器搭载的视觉管线系统。通过该方法，我们成功构建了该视觉管线的IPC，进而设计了一种利用所学IPC的控制算法，旨在实现四旋翼飞行器在着陆平台上的安全降落。实验表明，尽管感知模块存在误差，采用所学IPC的控制算法仍能实现四旋翼飞行器的安全着陆，而未使用所学IPC的基线算法则无法完成该任务。