Award Abstract # 2125858
NRT-AI: Convergent, Responsible, and Ethical Artificial Intelligence Training Experience for Roboticists
NSF Org: |
DGE
Division Of Graduate Education
|
Recipient: |
UNIVERSITY OF TEXAS AT AUSTIN
|
Initial Amendment Date:
|
August 27, 2021 |
Latest Amendment Date:
|
August 25, 2022 |
Award Number: |
2125858 |
Award Instrument: |
Continuing Grant |
Program Manager: |
Daniel Denecke
ddenecke@nsf.gov
(703)292-8072
DGE
Division Of Graduate Education
EDU
Directorate for STEM Education
|
Start Date: |
September 1, 2021 |
End Date: |
August 31, 2026 (Estimated) |
Total Intended Award
Amount: |
$2,999,999.00 |
Total Awarded Amount to
Date: |
$2,999,999.00 |
Funds Obligated to Date:
|
FY 2021 = $2,415,108.00
FY 2022 = $584,891.00
|
History of Investigator:
|
-
Junfeng
Jiao
(Principal Investigator)
jjiao@austin.utexas.edu
-
Luis
Sentis
(Co-Principal Investigator)
-
Joydeep
Biswas
(Co-Principal Investigator)
-
Min Kyung
Lee
(Co-Principal Investigator)
-
Justin
Hart
(Co-Principal Investigator)
|
Recipient Sponsored Research
Office: |
University of Texas at Austin
110 INNER CAMPUS DR
AUSTIN
TX
US
78712-1139
(512)471-6424
|
Sponsor Congressional
District: |
25
|
Primary Place of
Performance: |
University of Texas at Austin
310 Inner Campus Drive
Austin
TX
US
78712-1007
|
Primary Place of
Performance Congressional District: |
25
|
Unique Entity Identifier
(UEI): |
V6AFQPN18437
|
Parent UEI: |
|
NSF Program(s): |
NSF Research Traineeship (NRT)
|
Primary Program Source:
|
04002122DB NSF Education & Human Resource
04002223DB NSF Education & Human Resource
|
Program Reference
Code(s): |
9179,
SMET
|
Program Element Code(s):
|
199700
|
Award Agency Code: |
4900
|
Fund Agency Code: |
4900
|
Assistance Listing
Number(s): |
47.076
|
ABSTRACT

Given the potentially disruptive consequences of artificial intelligence (AI)-based systems, humanity cannot afford to wait until problems arise to consider their impacts on society. AI?s ethical and societal implications must be considered as systems are designed, developed, and deployed. The increasingly ubiquitous adoption of robots in homes and cities is poised to transform our society. However, it remains an open question whether this technology will develop in a way that increases the divide between haves and have-nots or results in a more just and equitable society. Thus, there is a need for convergent STEM graduate education to ensure that future roboticists are prepared to consider ethical implications of robotics technology and build a more just and equitable future for everyone. This National Science Foundation Research Traineeship (NRT) award to the University of Texas at Austin will address the challenge of integrating responsible and ethical AI at all stages of development, design, and deployment of service robots. The Convergent, Responsible, and Ethical AI Training Experience for Roboticists (CREATE Roboticists) program will integrate ethical robotics education, research, and career development. The program will train 32 funded trainees and 150 additional graduate students from the Departments of Aerospace, Computer Science, Electrical Engineering, and Mechanical Engineering, and Schools of Architecture, Information, and Public Affairs.
CREATE Roboticists will train future roboticists who: (i) understand the ethical implications of service robots and can develop new theories, methods, and techniques to satisfy ethical requirements; (ii) design human-centered ethical service robots that respect human autonomy and ethical values; and (iii) develop robotics policy informed by cutting edge convergent research. This program includes six elements: coursework, research opportunities, mentorship, professional development, internships, and public service. Interdisciplinary coursework will include five new courses, four of which are foundation courses, and a project-based capstone course. Trainees will engage in research projects across four domains: delivery systems, office service mobile robots, personal home robots, and industrial robots. Two faculty members will mentor each trainee over the five years of the program, with at least one mentor external to the student?s home department. Mentors and students will develop personalized individual development plans (IDPs) in the students? first year as trainees. They will revise these IDPs each semester in subsequent years of the program. The trainees will also participate in ten hours of career development workshops every semester on topics including article publication and grant-writing, startups and industry opportunities, and career planning. Trainees will enhance their education with internships at a private company, government, or non-profit organization. Finally, NRT trainees will spend about one day per month volunteering for a local government program or non-profit organization connected to robotics and AI.
The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
(Showing: 1 - 10 of 36)
(Showing: 1 - 36 of 36)
3. Chonkar, P.
"Look to my Lead: How Does a Leash Affect Perceptions of a Quadruped Robot?"
at the 2022 IEEE International Conference on Robotics and Automation (ICRA).
, 2022
Citation
Details
4. Morgenstein, K.
"What Goes Bump in the Night: Learning Tactile Control for Vision-Occluded Crowd Navigation"
2023 IEEE International Conference on Robotics and Automation (ICRA).
, 2023
Citation
Details
Caroline Wang, Ishan Durugkar
"DM2: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching"
Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23
, 2023
Citation
Details
Carson, Stark and Bohkyung, Chun and Casey, Charleston and Varsha, Ravi and Luis, Pabon and Surya, Sunkari and Tarun, Mohan and Peter, Stone and Justin, Hart
"Dobby: A Conversational Service Robot Driven by GPT-4"
IEEE International Symposium on Human-Robot Interactive Communication (RO-MAN)
, 2023
Citation
Details
Choi, J and Jiao, J
"Developing a Transit Desert Interactive Dashboard: Supervised Modeling for Forecasting Transit Deserts"
PLOS ONE
, 2024
Citation
Details
Choi, J and Jiao, J
"Measurement of Regional Electric Vehicle Adoption Using Multiagent Deep Reinforcement Learning. Applied Sciences"
Applied sciences
, 2024
Citation
Details
Choi, Yoonseo and Kang, Eun Jeong and Lee, Min Kyung and Kim, Juho
"Creator-friendly Algorithms: Behaviors, Challenges, and Design Opportunities in Algorithmic Platforms"
9. Creator-Friendly Algorithms: Behaviors, Challenges, and Design Opportunities in Algorithmic Platforms
, 2023
https://doi.org/10.1145/3544548.3581386
Citation
Details
Claure, Houston and Chang, Mai Lee and Kim, Seyun and Omeiza, Daniel and Brandao, Martim and Lee, Min Kyung and Jung, Malte
"Fairness and Transparency in Human-Robot Interaction"
17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
, 2022
https://doi.org/10.1109/HRI53351.2022.9889421
Citation
Details
Collier, C.
"AI as an Emancipatory Technology: Smart Hand Tools for Skilled Trade Workers"
57th Annual Hawaii International Conference on System Sciences.
, 2023
Citation
Details
Collier, C.
"Co-Designing Socio-Technical Interventions with Skilled Trade Workers"
IEEE International Symposium on Technology and Society, Public Interest Technology (PIT) for Innovation in Global Development Workshop
, 2023
Citation
Details
Collier, C.
"Skilled Workers, Knowledge Transfer, & AI."
HCI International, Developing Culturally Competent Autonomous Systems through Human-Centered Design Workshop
, 2023
Citation
Details
Gondimalla, Apoorva and Sreekanth, Varshinee and Joshi, Govind and Nelson, Whitney and Choi, Eunsol and Slota, Stephen C and Greenberg, Sherri R and Fleischmann, Kenneth R and Lee, Min Kyung
"Aligning Data with the Goals of an Organization and Its Workers: Designing Data Labeling for Social Service Case Notes"
, 2024
https://doi.org/10.1145/3613904.3642014
Citation
Details
Gonzalez, C.
"Design of a Person-Carrying Robot for Contact Compliant Navigation,"
ASME International Design Engineering Technical Conferences (IDETC 2023).
, 2023
Citation
Details
Hart, J.
"Longitudinal Social Impacts of HRI over Long-Term Deployments"
2022 ACM/IEEE International Conference on Human-Robot Interaction (HRI)
, 2022
Citation
Details
Jarrahi, M. H.
"Algorithmic Management: The Role of AI in Managing Workforces"
MIT Sloan Management Review
, 2023
Citation
Details
Jiao, J.
"Tracking Property Ownership Variance and Forecasting Housing Price with Machine Learning and Deep Learning"
IEEE International Conference on Big Data
, 2022
Citation
Details
Knox, WB and Hatgis-Kessell, S and Adalgeirsson, SO and Booth, S and Dragan, A and Stone, P and Niekum, S
"Learning Optimal Advantage from Preferences and Mistaking it for Reward."
Annual AAAI Conference
, 2024
Citation
Details
Lassiter, T.
"Human-Centered Design of AI-Enabled Smart Hand Tools."
HCI International, Developing Culturally Competent Autonomous Systems through Human-Centered Design Workshop
, 2023
Citation
Details
Lassiter, T.
"Welding instructors perspectives on using AI technology in welding training"
Proceedings of the 86th Annual Meeting of the Association for Information Science & Technology
, 2023
Citation
Details
Lee, Christine P and Lee, Min Kyung and Mutlu, Bilge
"The AI-DEC: A Card-based Design Method for User-centered AI Explanations"
, 2024
https://doi.org/10.1145/3643834.3661576
Citation
Details
Morgenstein, K.
"Learning Contact-based Navigation in Crowds"
2nd Workshop on Human-Interactive Robot Learning (HIRL 2023) at ACM/IEEE International Conference on Human-Robot Interaction (HRI
, 2023
Citation
Details
Phillips, Connor and Jiao, Junfeng
"Artificial Intelligence & Smart City Ethics: A Systematic Review"
IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS)
, 2023
https://doi.org/10.1109/ETHICS57328.2023.10154961
Citation
Details
Phillips, Connor and Jiao, Junfeng and Clubb, Emmalee
"Testing the Capability of AI Art Tools for Urban Design"
IEEE Computer Graphics and Applications
, v.44
, 2024
https://doi.org/10.1109/MCG.2024.3356169
Citation
Details
Ramesh, Divya and Henning, Caitlin and Escher, Nel and Zhu, Haiyi and Lee, Min Kyung and Banovic, Nikola
"Ludification as a Lens for Algorithmic Management: A Case Study of Gig-Workers Experiences of Ambiguity in Instacart Work"
Proceedings of ACM Conference on Designing Interactive Systems (DIS 2023
, 2023
https://doi.org/10.1145/3563657.3596004
Citation
Details
Seong, Kijin and Choi, Seung Jun and Jiao, Junfeng
"IoT sensors as a tool for assessing spatiotemporal risk to extreme heat"
Journal of Environmental Planning and Management
, 2024
https://doi.org/10.1080/09640568.2024.2320257
Citation
Details
Stapleton, Logan and Saxena, Devansh and Kawakami, Anna and Nguyen, Tonya and Ammitzbøll Flügge, Asbjørn and Eslami, Motahhare and Holten Møller, Naja and Lee, Min Kyung and Guha, Shion and Holstein, Kenneth and Zhu, Haiyi
"Who Has an Interest in Public Interest Technology?: Critical Questions for Working with Local Governments & Impacted Communities"
Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2022)
, 2022
https://doi.org/10.1145/3500868.3560484
Citation
Details
Tina L. Peterson, Rodrigo Ferreira
"Abstracted Power and Responsibility in Computer Science Ethics Education"
IEEE transactions on technology and society
, 2022
Citation
Details
W. Bradley Knox, Alessandro Allievi
"Reward (Mis)design for Autonomous Driving"
Artificial intelligence
, 2023
Citation
Details
Yoonchang, Sung and Rahul, Shome and Peter, Stone
"Asynchronous Task Plan Refinement for Multi-Robot Task and Motion Planning"
IEEE International Conference on Robotics and Automation (ICRA)
, 2024
Citation
Details
Zhang, Angie and Boltz, Alexander and Lynn, Jonathan and Wang, Chun-Wei and Lee, Min Kyung
"Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes"
Proceedings of the ACM/SIGCHI Conference on Human Factors in Computing Systems (CHI 2023)
, 2023
https://doi.org/10.1145/3544548.3581354
Citation
Details
Zhang, Angie and Rana, Rocita and Boltz, Alexander and Dubal, Veena and Lee, Min Kyung
"Data Probes as Boundary Objects for Technology Policy Design: Demystifying Technology for Policymakers and Aligning Stakeholder Objectives in Rideshare Gig Work"
, 2024
https://doi.org/10.1145/3613904.3642000
Citation
Details
Zhang, Angie and Walker, Olympia and Nguyen, Kaci and Dai, Jiajun and Chen, Anqing and Lee, Min Kyung
"Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation"
Proceedings of the ACM on Human-Computer Interaction
, v.7
, 2023
https://doi.org/10.1145/3579601
Citation
Details
Zifan, Xu and AmirHossain, Raj and Xuesu, Xiao and Peter, Stone
"Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning"
IEEE International Conference on Robotics and Automation
, 2024
Citation
Details
Ziping, Xu and Zifan, Xu and Runxuan, Jiang and Peter, Stone and Ambuj, Tewari
"4.Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks."
International Conference on Learning Representations (ICLR)
, 2024
Citation
Details
Zizhao, Wang and Caroline, Wang and Xuesu, Xiao and Yuke, Zhu and Peter, Stone
"Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning"
AAAI Conference on Artificial Intelligence
, 2024
Citation
Details
(Showing: 1 - 10 of 36)
(Showing: 1 - 36 of 36)
Please report errors in award information by writing to: awardsearch@nsf.gov.