Project Grant 1751770

This $349,000 National Science Foundation project grant supports research at Rice University to develop new robotics capabilities under uncertainty. The NSF Division of Civil, Mechanical, and Manufacturing Innovation is funding this three-year award through its Engineering program (CFDA 47.041). Specifically, the researchers will establish generic frameworks for robot manipulation systems to self-identify using exploratory motions while maintaining stability. By changing the traditional...

The National Science Foundation (NSF) awarded a $339,691 five-year CAREER grant (CFDA 47.070 - Computer and Information Science and Engineering) to North Carolina State University to develop lightweight formal methods and software tools that help mobile robot system developers build safer and more capable robots. The project aims to create techniques for inferring robotic system test cases from existing artifacts, as well as methods for understanding how physical units are used in robotic system...

This National Science Foundation Project Grant of $157,420 awarded in October 2022 under the Engineering (47.041) program will fund research to develop new algorithms for the automated computational design of compound machines containing multiple moving parts. Specifically, the principal investigator at Georgia Tech will create topology optimization frameworks to simultaneously design internal topologies of planar domains and optimize their connectivity to form multibody mechanisms. Finite...

This four-year Project Grant from the National Science Foundation Division of Information and Intelligent Systems provides $599,998 to the University of Michigan for research titled "Overcoming Epistemic Uncertainty to Plan with Learned Dynamics Models for Robotic Manipulation." Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the award will support the development of techniques to help robots plan complex manipulation tasks despite uncertainty...

The National Science Foundation (NSF) awarded a five-year, $361,090 CAREER grant under the Engineering program (CFDA 47.041) to Carnegie Mellon University (CMU) to develop new methods for enabling robots to better understand and respond to nuanced safety challenges in real-world environments. The research aims to expand the understanding of how robots can make safe decisions, moving beyond just collision avoidance to address more complex safety considerations, such as avoiding areas marked...

The National Science Foundation (NSF) awarded a $479,255 Project Grant under its Engineering program (CFDA 47.041) to The Trustees Of The University Of Pennsylvania, doing business as Clinical Practices Of The University Of Pennsylvania, to develop algorithms for automated design of folded robots with custom geometry and motion requirements. The goal is to simplify the complex process of designing folded mechanisms and robots by creating an end-to-end design system that can directly convert...

The Trustees of the University of Pennsylvania received a $500,000 Project Grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems on February 15, 2021 to support research titled "CAREER: TOWARDS A THEORY OF ROBUST LEARNING & CONTROL FOR SAFETY-CRITICAL AUTONOMOUS SYSTEMS" through January 31, 2026. This award will fund research under the NSF Engineering program (CFDA #47.041) to develop a theoretical framework for robust learning...

This National Science Foundation (NSF) Computer and Information Science and Engineering grant (CFDA 47.070) provides $50,000 to William Marsh Rice University to support research on enabling robust and dexterous robot manipulation capabilities for uncertain environments. The 5-year project, starting on June 1, 2023, explores a novel "manipulation funnels" framework to enable robots to handle uncertainties and unknowns through compliance- and motion-based strategies. This includes...

This National Science Foundation (NSF) CAREER grant award under the Engineering program (CFDA 47.041) provides $517,612 to the University of Texas at Austin to develop new foundations of scalable and resilient distributed reinforcement learning for real-time autonomous cooperation in open multi-agent systems. The project aims to design new learning and control methods that enable agents to interact effectively in open systems, adapt gracefully in time-varying environments, and be resilient to...

This three-year, $375,000 National Science Foundation project grant supports research at the University of Illinois to develop distributionally robust adaptive control methodologies for reinforcement learning algorithms. The goal is to enable safe and robust autonomous operation of complex systems by constructing a new class of adaptive controllers that are robust to errors in learned distributions. This will allow reinforcement learning algorithms to directly interact with the controllers...