The University of New Mexico received a three-year, $566,476 Project Grant from the National Science Foundation to support collaborative research on negotiated planning for stochastic control of dynamical systems. Funded under the NSF's Engineering program (CFDA 47.041), the award will allow researchers to improve methods for controlling uncertain dynamical processes through cooperative modeling and decision-making. The project directly supports the NSF Engineering directorate's goals of...
This $522,592 federal Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to create a data-driven autonomy framework that enables everyday workers to safely and effectively teach robots. The research will advance the state-of-the-art in dynamical systems and control through three interconnected thrusts: 1) developing a novel framework for data-driven control of complex, hard-to-model systems; 2) introducing methods for teaching robots through...
The National Science Foundation (NSF) awarded a $310,929 Project Grant under the Engineering program (CFDA 47.041) to the University of New Mexico (UNM) to develop novel computational tools and knowledge for safe autonomy of complex systems directly from data. The research aims to provide rigorous, data-driven guarantees on safety and performance, with a focus on automating ultrasound procedures to improve access to medical care. Key deliverables include techniques for synthesizing provably...
The National Science Foundation awarded a $721,021 project grant to the University of Michigan under the Engineering federal grant program (CFDA 47.041) to develop new foundations for multi-fidelity prediction, estimation, and learning under uncertainty in dynamical systems from September 1, 2023 through August 31, 2028. The University will conduct research to enable autonomous systems to estimate the effects of prediction uncertainty on planning and control decisions, with a focus on autonomous...
This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) project grant award of $299,998 to the University of New Mexico (UNM) will support the development of new formal methods for abstracting, verifying, and correcting learning-enabled cyber-physical systems. The research aims to improve the safety of AI-controlled systems built by NASA by computing size-reduced formal abstractions, conducting rigorous reachability analysis, and developing counterexample interpretation and...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) focuses on developing tools to ensure the safe operation of autonomous systems, such as robots and self-driving vehicles. The $375,000 award will be used by the University of Massachusetts to: 1) develop algorithms to align the learned norms of reinforcement learning agents with the intended design goals; 2) create formal verification...
The National Science Foundation awarded $199,723 under a Project Grant to San Diego State University Research Foundation for work titled "Towards Safe Aviation Autonomy: A Risk-Bounded Planning Framework for Dynamical Systems under Uncertainties". The two-year award runs from December 2021 through November 2023 under the Engineering program (CFDA 47.041). The Foundation will develop a risk-bounded planning and control framework for autonomous systems operating in uncertain...
This $300,000 National Science Foundation project grant supports research into distributed optimization-based control of large-scale nonlinear systems with uncertainties from 2022-2025. Funded under the NSF Engineering program (CFDA 47.041), the award to the University of California, San Diego will advance mathematical foundations for distributed optimization algorithms robust to uncertainties. Researchers will design tracking controllers for local systems to follow optimization-derived...
This $147,212 federal Project Grant awarded by the National Science Foundation's Engineering program (CFDA 47.041) supports research to enable the development of a framework for safe control in autonomous systems in the presence of sensor and actuator faults and cyber attacks. The research combines techniques from control theory, machine learning, and system security to provide provable safety guarantees across a range of autonomous systems and fault/attack scenarios. Key focus areas include...
This $800,000 National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant, awarded to the University of California, Berkeley on October 1, 2024, funds research to develop methods for certifying the safety of autonomous systems that use machine learning components. The key objectives are: (1) learning safety certificates and control policies, and (2) certifying the learned system. The project aims to use learning-based techniques to compute...
This $994,988 National Science Foundation project grant through the Engineering program (CFDA 47.041) will fund research at the University of New Mexico from May 1, 2023 through April 30, 2028. The research aims to develop an algorithmic framework for integrating knowledge of human perception and reasoning about uncertainty into the design and control of autonomous dynamical systems. New mathematical theory and computational algorithms will be created based on control theory, machine learning, and human factors to address stochasticity and accommodate human variability and context awareness. The algorithms and theories will be experimentally validated in simulation. This foundational work seeks to enable safe human-machine interaction and has potential for transformational impact in aerospace, manufacturing, transportation, and healthcare. It also supports culturally responsive teaching and research practices in engineering.