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2330040

Cooperative Agreement

Overview

Grant Description
Center: NSF Engineering Research Center for Human Augmentation via Dexterity (HAND)

The work of the NSF Engineering Research Center for Human Augmentation via Dexterity (HAND) will lead to versatile, dexterous robotic arms and hands that address a broad range of human, industry, and societal needs.

The purpose is to create robot manipulators that are widely useful “out of the box.”

Today, robot arms become useful only after an expensive integration process, making them inaccessible to many who might benefit, including most of the country’s quarter-million small and medium enterprises (SMEs).

To be useful out of the box, robots must have truly versatile end-effectors (“hands”), AI-powered dexterous skills, and intuitive interfaces that trained workers can use immediately.

Training must be widely accessible and career paths must be available to learners from a young age.

Firms of all sizes must be able to adopt these robots, and workers of all education levels, high school through postgraduate, must be able to use them.

The breadth and structure of the ERC program will enable HAND to address these technical, workforce, and ecosystem challenges, ultimately democratizing access to robot dexterity.

Robots will no longer be limited to high-volume, highly repeatable operations; they will find application in low-volume high-mix manufacturing, food processing, remote handling of precious or dangerous materials, assistance for individuals with motor impairments, and many other areas.

Widespread access to robotic manipulation will be vitally important as the U.S. addresses labor shortages in fields such as manufacturing and caregiving, and as demographics inexorably change, leading to a shrinking pool of workers supporting an aging population.

While some areas of robotics have seen dramatic advances in recent years, dexterous manipulation has proven to be a more challenging problem, requiring a very high level of convergence.

HAND will provide this with a convergent research program organized into three thrusts: hands (sensing, actuation, design), intelligent dexterity (simulation, representation, control), and human interface (multimodal interface, programming, social/legal/industrial studies).

The center will bring together experts in materials, manufacturing, manipulation, soft robotics, artificial intelligence, machine perception, modeling, haptics, human-robot interaction, participatory design and research, team science, education, law, and the social sciences to overcome fundamental barriers to dexterity.

These include achieving large scale integration of actuators and sensors, building robust visuo-tactile-motor skills that are composable into complex behaviors, and low-code programming by non-roboticists.

The result will be an engineered system – hands, skills, interface, and training materials – that dramatically advances robotic manipulation and its accessibility to diverse user populations.

HAND’s dexterous manipulators will be where AI learns about the physical world, and where AI is transformed to useful physical work.

Additionally, through engineering workforce development efforts, HAND will provide a novel education platform for introducing learners to AI and dexterity; an accelerator program to help SMEs succeed with robots; REU and RET programs that increase diversity; and undergraduate and graduate certificates built on a foundation of dexterity, social impacts of automation, and participatory research and design methods.

HAND will use those methods to engage diverse individuals, ensure that the benefits of dexterity are equitably shared, and promote diversity and a culture of inclusion.

HAND’s innovation ecosystem will support strong engagement with small, medium, and large manufacturers, robotics companies, national labs, civic organizations, and educators via testbeds, advisory boards, a robust process for technology transfer, and a public interest initiative.

Together with this ecosystem, HAND will impact the future of work by democratizing access to human augmentation via dexterity and framing the associated social, economic, and ethical implications.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.

Subawards are planned for this award.
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "GEN-4 ENGINEERING RESEARCH CENTERS", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF22580
Grant Program (CFDA)
Place of Performance
Evanston, Illinois 60208-3111 United States
Geographic Scope
Single Zip Code
Analysis Notes
Amendment Since initial award the total obligations have increased 129% from $3,500,000 to $8,000,000.
Northwestern University was awarded Revolutionizing Robotic Dexterity: NSF Engineering Human Augmentation Cooperative Agreement 2330040 worth $8,000,000 from Emerging Frontiers and Multidisciplinary Activities in September 2024 with work to be completed primarily in Evanston Illinois United States. The grant has a duration of 5 years and was awarded through assistance program 47.041 Engineering. The Cooperative Agreement was awarded through grant opportunity Gen-4 Engineering Research Centers.

Status
(Ongoing)

Last Modified 10/8/24

Period of Performance
9/1/24
Start Date
8/31/29
End Date
22.0% Complete

Funding Split
$8.0M
Federal Obligation
$0.0
Non-Federal Obligation
$8.0M
Total Obligated
100.0% Federal Funding
0.0% Non-Federal Funding

Activity Timeline

Interactive chart of timeline of amendments to 2330040

Subgrant Awards

Disclosed subgrants for 2330040

Transaction History

Modifications to 2330040

Additional Detail

Award ID FAIN
2330040
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
490705 DIVISION OF ENGINEERING EDUCATION
Funding Office
490704 OFFICE OF EMERGING FRONTIERS AND
Awardee UEI
EXZVPWZBLUE8
Awardee CAGE
39GV5
Performance District
IL-09
Senators
Richard Durbin
Tammy Duckworth
Modified: 10/8/24