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.
