利用回退安全MPC闭合运行时监视器的闭环
Closing the Loop on Runtime Monitors with Fallback-Safe MPC
AI总结：
本文提出结合鲁棒MPC与共形预测校准运行时监视器的框架，在感知退化时维持回退计划可行性，以极少样本实现新环境中感知使能系统的端到端安全认证。
摘要：
当我们依赖深度学习模型进行机器人感知时，必须认识到这些模型在与训练数据不相似的输入上可能表现不可靠，从而危及闭环系统的安全性。这引发了根本性问题：我们如何评估感知系统的置信度，以及当外部环境变化降低感知模型性能时，我们能在多大程度上采取保安全措施。因此，我们提出了一个框架，用于认证部署在新环境中的感知使能系统的安全性。为此，我们利用鲁棒模型预测控制（MPC）来控制系统，使用感知估计的同时维持一个不依赖感知系统的保安全回退计划的可行性。此外，我们使用最近提出的共形预测技术校准运行时监视器，以可认证地检测感知系统何时退化到超出MPC控制器容差的程度，从而实现端到端的安全保证。我们展示了该控制框架和校准技术使我们能够以比在新环境中部署感知网络所需重新训练样本少几个数量级的样本量来认证系统安全性，实验在照片级真实的飞机滑行模拟器上进行。此外，我们在四旋翼飞行器的仿真示例中展示了MPC的保安全行为。我们开源了仿真平台，并在项目页面提供了结果视频：https://tinyurl.com/fallback-safe-mpc。
英文摘要：
When we rely on deep-learned models for robotic perception, we must recognize that these models may behave unreliably on inputs dissimilar from the training data, compromising the closed-loop system's safety. This raises fundamental questions on how we can assess confidence in perception systems and to what extent we can take safety-preserving actions when external environmental changes degrade our perception model's performance. Therefore, we present a framework to certify the safety of a perception-enabled system deployed in novel contexts. To do so, we leverage robust model predictive control (MPC) to control the system using the perception estimates while maintaining the feasibility of a safety-preserving fallback plan that does not rely on the perception system. In addition, we calibrate a runtime monitor using recently proposed conformal prediction techniques to certifiably detect when the perception system degrades beyond the tolerance of the MPC controller, resulting in an end-to-end safety assurance. We show that this control framework and calibration technique allows us to certify the system's safety with orders of magnitudes fewer samples than required to retrain the perception network when we deploy in a novel context on a photo-realistic aircraft taxiing simulator. Furthermore, we illustrate the safety-preserving behavior of the MPC on simulated examples of a quadrotor. We open-source our simulation platform and provide videos of our results at our project page: https://tinyurl.com/fallback-safe-mpc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
