Project Grant 2533864
- Federal Grant Award Summary The National Science Foundation (NSF) Directorate for Engineering awarded the University of Texas at Arlington $299,963 on July 15, 2026, through its Engineering program (CFDA 47.041) to support collaborative research on scaling robust multi-agent systems. The project, which runs through June 30, 2029, will develop efficient learning methods and decentralized algorithms that enable autonomous multi-agent systems to perform reliably in real-world environments. The...
- Grant Summary Rensselaer Polytechnic Institute received a $299,935 collaborative research Project Grant awarded July 1, 2026, through the National Science Foundation's Directorate for Engineering (CFDA 47.041). The award funds research on decentralized multi-agent reinforcement learning (MARL) algorithms designed to enable autonomous decision-makers—such as robots, energy resources, and actuators—to learn safe and efficient operation in dynamic, uncertain environments without requiring a central...
- This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $356,488 to the Texas A&M Engineering Experiment Station (Tees) to develop a resilient reinforcement learning (RL) framework for managing heterogeneous multi-agent systems in complex and structured environments. The research aims to produce scalable and computationally-efficient RL algorithms with rigorous convergence and complexity analysis for applications like interference management...
- Federal Grant Award Summary Texas A&M University received a $192,286 Project Grant from the National Science Foundation's Division of Civil, Mechanical, and Manufacturing Innovation (Engineering program, CFDA 47.041) effective September 1, 2025 through August 31, 2028. This collaborative research initiative develops an adaptive human-robot collaboration framework designed to enhance worker-robot synergy in dynamic industrial environments such as construction and manufacturing. The project...
- Federal Grant Award Summary The National Science Foundation (NSF) Directorate for Engineering awarded $224,996 to the New Jersey Institute of Technology (NJIT) on July 1, 2026, for a collaborative research project titled "Reinforcement Learning with High-Probability Safety Constraints: Theory, and Applications" (CFDA 47.041). The three-year project, concluding June 30, 2029, will deliver foundational theory, algorithms, and software tools that enable safe reinforcement learning (RL)...
- Federal Project Grant Award Summary The University of Central Florida Board of Trustees received a $300,240 Project Grant from the National Science Foundation's Directorate for Engineering (CFDA 47.041) awarded July 15, 2026, with completion targeted for June 30, 2029. This collaborative research initiative focuses on developing robust multi-agent learning algorithms and decentralized control methods for autonomous systems operating in real-world environments. The project deliverables include...
- Federal Project Grant Award Summary Award Details: This $500,000 National Science Foundation (NSF) Engineering (CFDA 47.041) CAREER Project Grant was awarded to Texas A&M Engineering Experiment Station on April 15, 2026, with a completion date of March 31, 2031. The award supports research and development addressing the autonomous real-time coordination of ultra-large-scale converter-based distributed energy resources (C-DERs) in integrated transmission-distribution power systems. Products...
- Federal Project Grant Award Summary William Marsh Rice University received a $140,000 Project Grant from the National Science Foundation's Division of Civil, Mechanical, and Manufacturing Innovation under the Engineering program (CFDA 47.041), effective September 1, 2025, through August 31, 2028. This award supports the development of risk-sensitive learning-augmented adaptive control algorithms designed to address operational challenges in complex networked systems. The research will advance...
- The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded a $500,000 Project Grant to Texas A&M Engineering Experiment Station, doing business as Tees, to support research towards a principled framework for resilient, data efficient and scalable reinforcement learning for control. The award period is from February 1, 2021 through January 31, 2026. The research is funded under the NSF Directorate for Engineering's Engineering program (CFDA 47.041), which...
- This $375,000 federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The goal of the project is to develop tools and methods to help ensure the safe operation of autonomous systems that utilize reinforcement learning (RL) algorithms. Key activities include: 1) developing inverse RL algorithms to learn an agent's reward function from demonstrations, 2) exploring the agent's norms to...
Texas A&M Engineering Experiment Station received a $300,000 Project Grant from the National Science Foundation's Directorate for Engineering (CFDA 47.041) awarded July 1, 2026, through June 30, 2029, to develop decentralized multi-agent reinforcement learning (MARL) algorithms for constrained Markov games. The research will establish mathematical foundations and provably convergent algorithms that enable multiple autonomous decision-makers—such as robots, energy resources, or actuators—to learn and coordinate safely and efficiently in dynamic, uncertain, and competitive environments without reliance on centralized coordination. The project addresses a critical engineering challenge by explicitly incorporating safety and operational constraints into learning dynamics, advancing beyond existing approaches that typically assume full information sharing or ignore constraints during the learning process. Beyond its core technical contributions, the award supports national priorities in safe and trustworthy artificial intelligence and autonomous systems through integration of research outcomes into undergraduate and graduate curricula, student mentorship, and academic events focused on safe multi-agent learning. Educational and outreach activities are designed to broaden participation in STEM fields. The research outcomes are expected to enable deployment of autonomous systems in practical applications including multi-robot teams navigating shared spaces and distributed energy resource management systems.Federal Project Grant Award Summary
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.0k | 6/23/26 |