CPS:Medium: Safe Learning-Enabled Cyberphysical Systems

尽管机器学习取得了巨大进步，但设计真正自主的网络物理系统（CPS）、能够从环境中学习并与环境交互以实现复杂规范的目标仍然难以实现。 本研究旨在通过开发一类新型可验证的、支持安全学习的 CPS 来解决这一明显的悖论（机器学习的进步/相对较低的自主水平），该 CPS 能够适应以前未见过的动态场景，在系统运行时生成数据并必须做出决策。 它通过在动态系统、机器学习和可行性理论的融合处创建一个新框架，专门针对不确定的数据泛滥场景中的学习和安全行动而定制，解决了数据革命和高度动态系统带来的 CPS 挑战。该研究围绕三个紧密相互作用的主旨进行组织——R1：稀疏潜在特征和流形的联合学习，R2：动态场景中的实时推理； R3：可验证的决策算法——利用 CPS 动态引起的底层稀疏结构来快速解决挑战当前技术的问题。 所提出的框架的一个关键特征是它能够利用推力之间的紧密耦合来解决可处理的问题。例如，利用学习过程中揭示的简约结构的低复杂度实时推理方法，以及通过使用这些结构将问题重新转换为混合系统分析形式来验证闭环属性的控制策略。教育主动融入到该项目中。在大学预科阶段，将为城市高中生制定暑期 STEM 课程。参与者将探索 CPS 概念并完成一个最终项目，赋予自动驾驶汽车有限的学习能力。 在本科阶段，该提案中提出的想法将融入到课程中。该教育计划的标志是通过学习型 CPS 的中心隐喻进行整合。在研究生阶段，由联合PI代表的跨学科综合主题将继续下去，包括教授一门包含体验作业的课程。此外，该项目将为研究生提供成为跨学科团队成员的机会和支持。扩大参与的战略有两个方面：一方面，除了针对城市青年的夏季 STEM 项目外，还将利用 NUPRIME（NEU 的多元文化工程项目）。另一方面，它将利用联合PI在各自学会中的领导作用，在会议上组织针对高中生和代表性不足群体的活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

In spite of tremendous advances in machine learning, the goal of designing truly autonomous cyber-physical systems (CPS), capable of learning from and interacting with the environment to achieve complex specifications remains elusive. This research seeks to address this apparent paradox (advances in machine learning/relatively low levels of autonomy) by developing a new class of verifiable safe learning- enabled CPS, capable of adapting to previously unseen dynamic scenarios where the data is generated, and decisions must be made, as the system operates. It addresses the CPS challenges posed by the data revolution and highly dynamic systems by creating a new framework at the confluence of dynamical systems, machine learning and viability theory, specifically tailored to learning and safely acting in uncertain, data deluged scenarios.The research is organized around three tightly interacting thrusts -- R1: Joint learning of sparse latent features and manifolds, R2: Real-time inference in dynamic scenarios; and R3: Verifiable decision-making algorithms -- that exploit the underlying sparse structure induced by the dynamics of the CPS to obtain fast solutions to problems that challenge current techniques. A key feature of the proposed framework is its ability to take advantage of the tight coupling between thrusts to obtain tractable problems. Examples are low-complexity real-time inference methods that leverage parsimonious structures unveiled during learning, and control strategies that verify closed-loop properties by using these structures to recast the problem into a hybrid system analysis form.Education is proactively integrated into this project. At the pre-college level, summer STEM programs for urban high school students will be developed. Participants will explore CPS concepts and complete a final project endowing autonomous vehicles with limited learning capabilities. At the undergraduate level, ideas put forth in this proposal will be infused through the curriculum. The hallmark of the educational program will be its integration through the central metaphor of learning-enabled CPS. At the graduate level, this integrative theme across the disciplines represented by the Co-PIs will be continued, including teaching of a course that includes experiential assignments. In addition, this project will provide opportunities and support for graduate students to engage as members of an interdisciplinary team. The strategy to broaden participation is two pronged: on one hand, it will leverage, in addition to the summer STEM programs for urban youth, NUPRIME (NEU's Program in Multicultural Engineering). On the other hand, it will take advantage of the co-PIs leadership roles in their respective societies to organize events targeting high schoolers and underrepresented groups at conferences.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.