Project Grant 2550106
- This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program, totaling $209,267, will fund research to close the simulation-to-reality (sim-to-real) gap in reinforcement learning (RL). The research will develop new techniques using randomization, alignment, and derivation mechanisms to improve the applicability and generalization of RL systems from simulated to real-world environments. The goal is to...
- This Project Grant award of $299,631 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research at Purdue University focused on integrating foundational AI models into reinforcement learning (RL) algorithms. The goal is to develop new theoretical frameworks and methods that allow RL systems to acquire new skills more quickly, perform better in unfamiliar situations, and be deployed more rapidly in real-world...
- This National Science Foundation (NSF) project grant under the Computer and Information Science and Engineering (CISE, CFDA 47.070) program addresses critical challenges in applying reinforcement learning (RL) to real-world urban environments. The $353,369 project, awarded to Arizona State University (UEI: NTLHJXM55KZ6), aims to develop actionable data analytics tailored to urban decision-making, focusing on issues like noisy/incomplete observations, complex system behaviors, and the need for...
- This $299,999 Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to enhance the performance of reinforcement learning (RL) systems in completing complex tasks in challenging environments. The project aims to develop new task and environment representations to enable active learning strategies that optimize resource allocation and reduce the need for extensive physical interactions with the...
- This National Science Foundation (NSF) Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $816,735 to Northeastern University to research techniques for improving the sample efficiency of reinforcement learning and imitation learning for robotic manipulation tasks. The key goals are to: 1) expand symmetric learning methods to handle imperfect symmetries; 2) explore object-factored symmetric models; 3) explore symmetric learning in...
- 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...
- This $249,987 Project Grant awarded by the National Science Foundation (CFDA 47.070 Computer and Information Science and Engineering) supports a collaborative research initiative to develop a mathematical foundation for deep reinforcement learning (DRL). The key objectives are to characterize the computational and statistical complexity of DRL, and to design more efficient and reliable methods for DRL applications in real-world domains such as robotics, healthcare, and transportation systems....
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $300,000 to Arizona State University (ASU) from August 2024 to July 2027 aims to enhance the performance of reinforcement learning (RL) systems in completing difficult tasks in complex environments. The project seeks to develop task and environment representations specifically for active design in RL, including: 1) Active Environment Design for RL to...
- This Project Grant award, valued at $569,138, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The award supports the development of new methods for actively testing autonomous decision-making systems that utilize reinforcement learning (RL) algorithms. The key objectives are to derive optimal policies for evaluating RL-based autonomous systems, create novel adaptive sampling algorithms to improve policy...
- The National Science Foundation (NSF) awarded a $375,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the Regents of the University of Michigan, Office of Research and Sponsored Projects, doing business as the University of Michigan. The grant, awarded on October 1, 2023, aims to develop foundational technologies for safe Reinforcement Learning (RL)-enabled systems, integrating research and education. The project focuses on three key thrusts: (1)...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program provides $290,739 to Clemson University to conduct research aimed at closing the simulation-to-reality (sim-to-real) gap in reinforcement learning (RL). The research will explore three mechanisms - randomization, alignment, and derivation - to improve the performance of RL systems when transitioning from simulated training environments to real-world deployment. The goal is to enhance the availability, applicability, and generalization of RL techniques, minimizing the gap between common RL practices and real-world applications. This 2-year project, running from September 1, 2025 to August 31, 2027, will benefit the development of next-generation RL technologies that can more effectively bridge the sim-to-real divide.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $290.7k | 9/10/25 |