Collaborative Research: SLES: Guaranteed Tubes for Safe Learning across Autonomy Architectures

自行运行的自主系统(例如自动驾驶汽车和无人送货无人机)面临着取决于任务和环境复杂性的安全挑战。尽管这样的系统已经显示出自己学习和适应的能力，但确保这些系统的安全并不是一件微不足道的事情。安全可能因各种因素而受到威胁，包括但不限于意外变化、恶劣天气条件或未知障碍。这些系统的车载学习解决方案大多用于非关键场合，如电脑游戏，在这些场合，安全问题最小。这项提议旨在解决各种应用场景中对学习型系统端到端安全的迫切需求，例如城市空中机动性中的自动驾驶汽车和飞行车辆。我们提出了一种新的解决方案，称为“数据启用单工”或“去单工”。DeSimplex建立在坚实的数学原理和系统方法的基础上，用于收集数据并使用这些数据来提高系统的性能。它提供了一个可被证明是安全的框架，并允许支持学习的系统即使在面临极端事件或环境危害时也能进行调整并正常运行。这项拟议的工作对于涉及自主系统在不可预测的、苛刻的物理环境中安全高效运行的更广泛应用至关重要，包括自动驾驶汽车和3D城市空中机动的飞行车辆。拟议的工作为增进对端到端学习系统安全性的理解奠定了基础，端到端学习系统是网络物理系统、机器人学和机器学习中的一个基本问题。我们的目标是追求以下两个相互关联的研究方向：(I)用可靠的不确定性量化方法提高高性能自主性，以确保数据驱动的适应性和不确定性的精确测量；(Ii)开发高保证的自主性体系结构，并为可验证的可观性和可控性建立切换规则。将开发一个框架，结合高性能和高保证自治的优点，促进自适应学习、准确的不确定性量化和可验证的安全措施。将开发新的方法，用于(I)基于策略的闭环学习以提高性能，(Ii)可靠的不确定性量化以提供数据驱动的适应性，以及(Iii)在系统级别上可验证的可观测性和可控性。对于拟议的高性能自主性，将采用系统的双策略方法进行安全数据收集，以协调数据的三个所需属性：安全性、按策略和闭环系统。建议的框架将在从模块化模拟测试到在真实空中和地面车辆上的集成和部署的严格程序中得到验证。这项研究得到了国家科学基金会和开放慈善机构之间的合作伙伴关系的支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The autonomous systems that operate by themselves (e.g., autonomous cars and delivery drones) expose safety challenges dependent upon the complexity of the missions and environments. Although such systems have demonstrated the ability to learn and adapt on their own, ensuring the safety of these systems is not trivial. Safety can be endangered due to various factors, including but not limited to unexpected changes, inclement weather conditions, or unknown obstacles. The onboard learning solutions of these systems have mostly been used in non-critical situations like computer games, where safety concerns are minimal. This proposal aims to address the urgent need for end-to-end safety in learning-enabled systems across various application scenarios, e.g., self-driving cars and flying vehicles in urban air mobility. We propose a novel solution called "Data-enabled Simplex" or "DeSimplex.” DeSimplex is built on solid mathematical principles and systematic methods for collecting data and using the data for the system’s performance improvement. It provides a framework that can be proven to be safe and allows learning-enabled systems to adjust and perform well even when faced with extreme events, or environmental hazards. The proposed work is crucial for wider applications that involve the safe and efficient operation of autonomous systems in unpredictable, demanding physical environments, including autonomous cars and flying vehicles of 3D urban air mobility.The proposed work lays the groundwork for advancing the comprehension of safety in end-to-end learning-enabled systems, a foundational problem in cyber-physical systems, robotics, and machine learning. We aim to pursue the following two interconnected research thrusts: (i) improving high-performance autonomy with reliable uncertainty quantification methods to ensure data-driven adaptability and precise measurement of uncertainties and (ii) developing high-assurance autonomy architectures and establishing switching rules for verifiable observability and controllability. A framework will be developed that combines the strengths of high-performance and high-assurance autonomy, facilitating adaptive learning, accurate uncertainty quantification, and verifiable safety measures. Novel methods will be developed for (i) on-policy, closed-loop learning to boost performance, (ii) reliable uncertainty quantification to provide data-driven adaptability, and (iii) verifiable observability and controllability at the system level. A systematic, dual-strategy approach will be pursued for safe data collection for the proposed high-performance autonomy to reconcile the three desired properties for data: safety, on-policy, and closed-loop. The proposed framework will be validated in a rigorous procedure from modular simulation testing to integration and deployment on real aerial and ground vehicles.This research is supported by a partnership between the National Science Foundation and Open Philanthropy.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.