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 physical...
This $800,000 Project Grant award to The Trustees of Princeton University, Department of Research and Project Administration, was provided by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The award aims to develop new techniques for constructing a vision-based safety supervisor that can endow autonomous robotic systems, such as self-driving cars and home robots, with the safety property of "graceful degradation."...
This federal Project Grant award of $800,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) is supporting research to develop methods for certifying the safety of autonomous systems that use deep learning-enabled perception, prediction, and control components. The key goals of the project are: (1) to develop techniques for learning safety certificates and control policies for these types of learning-enabled autonomous...
This federal Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $193,000 to the Trustees of Princeton University over a 3-year period starting September 1, 2024. The funding supports collaborative research on developing safe reinforcement learning techniques that can be applied in domains like robotics, autonomous driving, and power systems. The key research thrusts include: 1) training robust policies using distributionally robust approaches;...
This Project Grant award for $50,000.00, provided by the National Science Foundation (NSF) Engineering program (CFDA 47.041), aims to develop a framework for learning complex, long-horizon tasks from few-shot vision-language demonstrations. The primary objective is to enable robots to learn personalized tasks through natural interactions with users, who can provide visual demonstrations combined with language narration. The research leverages large language models to summarize vision-language...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, CFDA 47.070, supports a research project titled "CAREER: ACTIVE SCENE UNDERSTANDING BY AND FOR ROBOT MANIPULATION" at Stanford University. The $130,000 grant, awarded on October 1, 2023, aims to develop a self-improving robot perception system that leverages manipulation skills for active scene understanding. The key focus is on enabling robots to...
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 $50,000 Project Grant was awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) to The Leland Stanford Junior University (Stanford University) to develop a novel approach for robotic dexterous manipulation that leverages the powerful new object representation of neural radiance fields (NeRFs). The primary objective is to create an algorithmic pipeline that can go from camera pixels to grasp to manipulation trajectory, enabling complex manipulation skills such as...
This Project Grant awarded by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) will support $361,090 in research to expand the understanding of how robots can make safe decisions in real-world environments. The goal is to develop new methods that enable robots to better assess and respond to nuanced safety challenges, such as avoiding areas marked with caution tape, slowing down when transporting hot liquids, and seeking clarification when uncertain about a task....
This $466,714 project grant from the National Science Foundation's Engineering program (CFDA 47.041) supports the development of new expressive and differentiable robotics simulators at Stanford University from September 2022 through August 2026. The researchers will establish the mathematical foundations for simulators with learnable components, prototype such simulators, and evaluate their ability to reduce the "sim-to-real gap" where robots perform differently in simulation versus...