CAREER: Perceivability: Enabling Safe and Secure Autonomy via Synergistic Control, Observation and Learning

这项学院早期职业发展(Career)补助金将通过建立感知能力的概念来解决控制和评估理论中的基本问题：感知能力是系统的结构属性，描述了在面临限制的情况下动态构建环境知识的能力。自动驾驶系统(如无人机、自动驾驶汽车)必须能够安全和及时地学习它们所在的环境，使它们能够安全地与人类和彼此交互。该过程取决于系统的结构(例如，动态、约束)和目标，以及由于传感器故障而不可靠或由于恶意行为而不可信的信息。基本的可感知问题是：“在给定的学习算法下，在给定的时间范围内，针对给定的动态、感知和通信能力，安全地学习给定的环境是可行的吗？”如果答案是否定的，那么人们可能会想：“系统的哪些参数可以改变，以便学习环境？控制和观察之间的协同效应如何才能安全地促进知识的产生？该项目将发展感知能力和计算效率学习和控制技术的基础，以提高系统的安全性、自主性和复原力。它将得到一个教育和外展计划的补充，该计划将吸引代表人数不足的群体和K-12学生，并通过外展活动和机构STEM计划传播结果。感知能力在系统科学中引入了一个改变游戏规则的概念，旨在弥合学习、估计和控制之间的差距，并使系统工程中的新能力成为可能。如果存在安全的控制输入，从而存在物理系统的安全轨迹，从而能够收集能够学习环境的数据，则环境称为在某一时间范围内可感知的环境。因此，可感知性可以被认为是智能系统的广义属性：可达性和可观测性的结合，将知识构建过程与系统动力学和约束紧密联系在一起。该项目将调查系统结构和基本控制、估计和学习机制(I)如何能够在使用不确定(即，错误或恶意)信息的同时，在安全的系统轨迹上，在给定的时间范围内确定环境是否可感知的能力，以及(Ii)如何改变系统结构和/或知识建立机制，以实现安全的知识生成。这些创新将使自主系统能够在安全关键和时间关键的情况下完成智能、复杂的任务。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。

This Faculty Early Career Development (CAREER) grant will address fundamental questions in control and estimation theory by establishing the concept of perceivability: the structural property of a system that describes the ability to build knowledge about an environment dynamically, in the face of constraints. Autonomous systems (e.g., drones, self-driving cars) must be able to safely and timely learn the environment they operate in, to enable them to interact safely with humans and each other. This process depends on the structure (e.g., dynamics, constraints) and goals of the system, and information that is unreliable due to sensor faults, or untrusted due to malicious actions. The fundamental perceivability question is: “Is it feasible to safely learn a given environment for given dynamics, sensing and communication capabilities, under a given learning algorithm, within a given time horizon?” If the answer is negative, then one may wonder: “What parameters of the system can be changed such that the environment can be learned? What are the synergies between control and observation that safely enhance the generation of knowledge?” The project will develop the foundations of perceivability, and computationally-efficient learning and control techniques towards increasing system safety, autonomy and resilience. It will be complemented by an educational and outreach program that will engage underrepresented groups and K-12 students and disseminate the results via outreach activities and institutional STEM programs. Perceivability introduces a game-changing concept in systems science that aims to bridge the gap between learning, estimation and control, and enables new capabilities in systems engineering. An environment is called perceivable within some time horizon if there exists a safe control input, and therefore a safe trajectory of the physical system, that enables the collection of data over which the environment can be learned. Perceivability can thus be thought of as a generalized property of an intelligent system: a merging of reachability and observability that tightly links the knowledge-building process with the system dynamics and constraints. The project will investigate how the system structure and the underlying control, estimation and learning mechanisms (i) enable the ability to characterize whether an environment is perceivable within a given time horizon, over safe system trajectories while using uncertain (i.e., faulty or malicious) information, and (ii) how the system structure and/or the knowledge-building mechanism can be altered to achieve safe knowledge generation. The innovations will enable autonomous systems to accomplish intelligent, complex tasks in safety-critical and time-critical situations.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.