This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports collaborative research to design provably safe autonomous systems. The $375,000 award to the University of Texas at Austin aims to develop tools that can align the norms and behaviors of reinforcement learning (RL) agents with the intent of their designers. Key activities include developing inverse RL algorithms to learn agent reward functions,...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) Project Grant award of $750,000 to Oregon State University (OSU) will develop tools to design and verify the safety of autonomous systems employing reinforcement learning. The key activities include:
Developing inverse reinforcement learning algorithms to align the agent's norms with the designers' intent, constrained by deontic logic.
Exploring the agent's norms to uncover...
This $749,963 Project Grant was awarded by the National Science Foundation (NSF) Division of Computing and Communication Foundations on October 1, 2023. The grant is funded under the NSF's Computer and Information Science and Engineering (CFDA #47.070) program, which supports investigator-initiated research and education across all areas of computing, communications, and information science and engineering.
The grant was awarded to Northeastern University, a private, non-profit research...
This $425,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop a framework for ensuring the safety of future robotic systems. The research project, led by The Trustees of Princeton University, aims to lay the foundation for safe robot autonomy by enabling robots to continually prove the safety of their actions under a wide range of operating conditions, from complex...
This National Science Foundation (NSF) Project Grant award under CFDA 47.041 - Engineering, in the amount of $25,000.00, will fund research on "Introspective Counterfactual Reasoning for Robust and Resilient Autonomy." The University of Massachusetts (UMass) is the primary awardee and will lead this collaborative research project to develop novel capabilities that empower robots with "lifelong autonomy" through enhanced counterfactual reasoning. The research aims to create...
This $500,000 project grant, awarded on January 1, 2024 by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to address the urgent need for end-to-end safety in learning-enabled autonomous systems across various application scenarios, such as self-driving cars and urban air mobility.
The project, titled "COLLABORATIVE RESEARCH: SLES: GUARANTEED TUBES FOR SAFE LEARNING ACROSS AUTONOMY ARCHITECTURES,"...
This $1,500,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports the "SLES: SPECSRL: SPECIFICATION-GUIDED PERCEPTION-ENABLED CONFORMAL SAFE REINFORCEMENT LEARNING" research project at the University of Pennsylvania.
The key objectives of this 4-year project are to develop novel techniques for specification-guided reinforcement learning (RL) with robust safety guarantees, to enable the safe...
This $1,500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports the University of Southern California's research on safe multi-agent systems using a neurosymbolic approach. The project aims to develop new theories and algorithms for the design of safe learning-enabled multi-agent systems, with applications in areas like wildfire prevention using drone swarms and semi-automated...
This $270,913 federal Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program, supports research to develop qualitative and quantitative methodologies for assessing the safety of learning-enabled autonomous systems. The project, led by the Augusta University Research Institute, Inc. (AURI), will target foundational challenges in capturing uncertainties from environments and providing timely, comprehensive,...
This $800,000 Project Grant was awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the University of California, Berkeley. The grant supports research and development to create new techniques for certifying the safety of robotic systems that utilize learning-enabled components, such as neural network-based control mechanisms. Key objectives of the project include: (1) developing methods to learn safety...