CAREER: Safety Assurances for Perception-Enabled Robotic Systems

从自动驾驶汽车到自动驾驶无人机，机器学习驱动的感知组件构成了现代自主系统和机器人的核心部分。自主系统的能力主要是通过现代机器学习方法来优雅地处理丰富的感知输入和输出，从而产生有用的控制信息，最终使机器人能够在新的情况下根据他们所看到的做出智能决策。然而，感知故障可能会导致灾难性的机器人故障并危及人类安全，最近的自动驾驶汽车事故就是一个例子。因此，确保机器人在学习驱动的、基于感知的控制器下安全运行，对于使其在高完整性和安全关键型应用中得以采用至关重要。该项目将建立一个基础框架，为基于感知的控制器下的闭环系统提供持续的安全保证，其中保证在训练时临时提供，并在操作时间（或运行时间）期间持续监控，更新和改进。特别是，该项目将：（a）开发新的技术，用于学习鲁棒的构造感知策略;（B）构建感知策略的安全监控器，以确保其在运行时的安全操作;（c）开发一种原则性的方法来大规模挖掘闭环感知故障，并使用它们来提高机器人的安全性。这些结果将通过对异构物理机器人测试平台以及真实感模拟器的全面评估来实现，重点是自主检查和自主飞机着陆任务。开发安全感知驱动系统的能力将对安全性和可靠性非常重要的广泛机器人应用产生直接的积极影响，例如关键基础设施的监控，服务或交付机器人以及自动汽车。这一影响将通过以下方式得到加强：（a）旨在促进机器人安全讨论并教育各级教师和学生的综合教育和推广计划：K-12，本科生和研究生以及更广泛的机器人研究社区;（B）与行业和监管机构密切合作;以及（c）侧重于传播代码库和执行情况，以及一个新的机器人课程的开源课程材料，包括手-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

From self-driving vehicles to autonomous drones, machine learning-driven perception components constitute a core part of modern autonomous systems and robots. Autonomous system capabilities are primarily enabled by the ability of modern machine learning methods to elegantly process rich perceptual inputs and outputs so as to produce useful information for control, ultimately enabling robots to make intelligent decisions in novel situations based on what they see. However, perception failures can cascade to catastrophic robot failures and compromise human safety, as exemplified by recent self-driving car accidents. Therefore, ensuring safe robot operation under learning-driven, perception-based controllers is paramount to enable their adoption in high-integrity and safety-critical applications. This project will establish a foundational framework for providing continual safety assurances for closed-loop systems under a perception-based controller, wherein assurances are provided provisionally at training time, and continually monitored, updated, and improved during operation-time (or runtime). In particular, this project will: (a) develop novel techniques for learning robust-by-construction perception policies; (b) construct safety monitors for perception policies to ensure their safe operation during runtime; and (c) develop a principled approach to mine closed-loop perception failures at scale and use them to improve robot safety over time. These results will be grounded through a thorough evaluation on a heterogeneous physical robotic testbed, as well as photorealistic simulators, with a focus on autonomous inspection and autonomous aircraft landing tasks. The ability to develop safe perception-driven systems will have a direct, positive impact on a broad range of robotics applications where safety and reliability are of high importance, such as surveillance of critical infrastructure, service or delivery robots, and autonomous cars. This impact will be enhanced through: (a) an integrated education and outreach plan designed to facilitate robot safety discussions and educate faculty and students at all levels: K-12, undergraduate and graduate students, and the broader robotics research community; (b) close collaborations with industry and regulatory bodies; and (c) focusing on disseminating codebases and implementations, and open-sourcing curriculum materials for a new robotics course including hands-on labs with wheeled robots.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.