This Project Grant award, valued at $225,509.00, was provided by the National Institute of Standards and Technology (NIST) under the Measurement and Engineering Research and Standards (CFDA 11.609) Federal Grant Program. The purpose of this award is to develop novel motion planning and control algorithms that can optimize physical interactions for improved robotic manufacturing capabilities. Key activities include creating adaptive motion and task-level controllers that can compliantly execute...
This $116,985 Project Grant award from the National Institute of Standards and Technology (NIST) under the Measurement and Engineering Research and Standards (CFDA 11.609) program supports the development of new methods for rapidly assessing the absolute accuracy of robotic manipulators within their workspaces. The key activities include: Developing new sampling methodologies to efficiently explore a robot's workspace and improve its position and orientation accuracies; determining the optimal...
This National Science Foundation Project Grant of $299,923 awarded on September 15, 2022 will fund the development of an open-source ecosystem for robot manipulation research through August 31, 2023. The award falls under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) which aims to advance science and technology innovation. The University of Massachusetts Lowell will establish an open-source ecosystem covering physical, digital, instructional and functional assets to...
This Cooperative Agreement award from the National Institute of Standards and Technology (NIST), under the Measurement and Engineering Research and Standards (CFDA 11.609) Federal Grant Program, provides $251,139.07 to Prospicience, LLC to conduct research to help the NIST Measurement Science for Manufacturing Robotics (MSMR) program develop a better understanding of machine learning (ML) tools and techniques for more efficient and effective robot programming. The key activities to be...
The National Science Foundation (NSF) awarded a $1,493,117 Project Grant under the NSF Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) to the University of Massachusetts Lowell. The grant, effective July 1, 2024 through June 30, 2026, will fund the development of the Collaborative Open-Source Manipulation Performance Assessment for Robotics Enhancement (COMPARE) ecosystem. This ecosystem aims to improve the effectiveness of robot perception and grasping by establishing...
The University of Massachusetts Lowell will receive $199,556 under a cooperative agreement award from the National Institute of Standards and Technology (NIST) Measurement and Engineering Research and Standards program (CFDA 11.609) to assist with the promotion and adoption of the Manufacturing Objects and Assemblies Dataset (MOAD). Specifically, UMass Lowell will replicate the MOAD data collection rig to evaluate documentation quality, distribute the MOAD object set including four assembly task...
The National Science Foundation awarded a $171,743 Project Grant to the University of Massachusetts Lowell under the Engineering program (CFDA 47.041). The grant will support research collaborating between UMass Lowell and Tufts University researchers to develop machine learning tools for analyzing student-written justifications for challenging concept questions in engineering statics courses. Over a three-year period from April 2023 through March 2026, researchers will collect student...
This three-year, $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop strategies for robotic manipulation without prior shape models of objects. The Massachusetts Institute of Technology will receive funding to design a state-estimation and task planning system that combines neural network perception with uncertainty modeling. This will enable robots to operate in less restrictive, real-world environments like...
This $140,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to revolutionize teleoperation technology for dexterous in-hand manipulation in human-robot collaboration. The primary research objectives are to: 1) develop a learning-based robot control policy to interpret human commands using end-effect-based task features, 2) create a safety-aware, multi-modal perception system to optimize sensory inputs for intuitive and safe operation, and...
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...