Award Abstract # 2128623
FW-HTF-R: Collaborative Research: Partnering Workers with Interactive Robot Assistants to Usher Transformation in Future Construction Work

NSF Org: SES
Division of Social and Economic Sciences
Recipient: REGENTS OF THE UNIVERSITY OF MICHIGAN
Initial Amendment Date: August 25, 2021
Latest Amendment Date: December 11, 2023
Award Number: 2128623
Award Instrument: Standard Grant
Program Manager: Songqi Liu
soliu@nsf.gov
 (703)292-8950
SES
 Division of Social and Economic Sciences
SBE
 Directorate for Social, Behavioral and Economic Sciences
Start Date: January 1, 2022
End Date: December 31, 2025 (Estimated)
Total Intended Award Amount: $1,580,000.00
Total Awarded Amount to Date: $1,580,000.00
Funds Obligated to Date: FY 2021 = $1,580,000.00
History of Investigator:
  • Carol Menassa (Principal Investigator)
    menassa@umich.edu
  • Joyce Chai (Co-Principal Investigator)
  • Vineet Kamat (Co-Principal Investigator)
  • Wesley Mcgee (Co-Principal Investigator)
  • Arash Adel (Former Co-Principal Investigator)
Recipient Sponsored Research Office: Regents of the University of Michigan - Ann Arbor
1109 GEDDES AVE STE 3300
ANN ARBOR
MI  US  48109-1015
(734)763-6438
Sponsor Congressional District: 06
Primary Place of Performance: University of Michigan
2350 Hayward Street, Civil and E
Ann Arbor
MI  US  48109-1003
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): GNJ7BBP73WE9
Parent UEI:
NSF Program(s): FW-HTF Futr Wrk Hum-Tech Frntr
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 063Z
Program Element Code(s): 103Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.075

ABSTRACT

Construction is a $10 trillion industry that employs about 180 million workers worldwide. However, the future of construction work is at crossroads. First, productivity in construction work has been stagnant relative to other industries (e.g., manufacturing), and the industry has historically been slow to adopt innovations that affect efficiency. Second, it has been difficult to offset the aging and retiring workforce with younger and more diverse workers, causing the workforce supply to fall short of rising demand. This is mainly because construction work tends to be physically strenuous leading to occupational hazards that often force workers to retire early. Robotization has been suggested as a potential solution to these problems. However, the unstructured nature of construction work presents several technical, social and economic impediments that hinder the direct adoption and integration of such innovations by the construction industry. For construction workers, robotic technology can only be transformative if it allows them to channel their passion for the work while avoiding the chronic pain and health outcomes associated with its physical demands.

This project investigates if construction work can be conceived as a human-robot partnership, where human workers play the critical role of planning the work, and training and supervising robotic assistants to adapt to presented workspace conditions and perform useful work. The project team is integrating advances in interactive task learning, mixed reality, and reinforcement learning to enable construction workers to naturally collaborate with robot assistants through direct physical interaction and virtual supervision and training. For such a symbiotic human-robot partnership to benefit construction workers and result in widespread deployment, workers need to be equipped with new skills. The project team is exploring new educational and professional development programs to support worker aspirations for upskilling and lifelong learning, and to open avenues for people of diverse abilities to be productive members of the construction workforce. Tight-knit partnerships with industry collaborators will inform the project activities and provide access to construction work sites and training facilities for testing and evaluation.

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

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(Showing: 1 - 10 of 23)
Adel, Arash and Ruan, Daniel and McGee, Wesley and Mozaffari, Salma "Feedback-driven adaptive multi-robot timber construction" Automation in Construction , v.164 , 2024 https://doi.org/10.1016/j.autcon.2024.105444 Citation Details
Cristian-Paul Bara, Ziqiao Ma "Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue" , 2023 Citation Details
Dai, Yinpei and Peng, Run and Li, Sikai and Chai, Joyce "Think, Act, and Ask: Open-World Interactive Personalized Robot Navigation" , 2024 https://doi.org/10.1109/ICRA57147.2024.10610178 Citation Details
Liang, Ci-Jyun. "Human-Robot Collaboration in Construction: Classification and Research Trends" Journal of construction engineering and management , v.147 , 2021 Citation Details
Liang, Ci-Jyun. "Trajectory-Based Skill Learning for Overhead Construction Robots Using Generalized Cylinders with Orientation" Journal of Computing in Civil Engineering , 2021 Citation Details
Liang, Ci-Jyun and McGee, Wes and Menassa, Carol C. and Kamat, Vineet R. "Real-time state synchronization between physical construction robots and process-level digital twins" Construction Robotics , v.6 , 2022 https://doi.org/10.1007/s41693-022-00068-1 Citation Details
Park, Somin. "A Comprehensive Evaluation of Factors Influencing Acceptance of Robotic Assistants in Field Construction Work" Journal of management in engineering , v.139 , 2023 Citation Details
Park, Somin and Menassa, Carol C and Kamat, Vineet R "Integrating Large Language Models with Multimodal Virtual Reality Interfaces to Support Collaborative HumanRobot Construction Work" Journal of Computing in Civil Engineering , v.39 , 2025 https://doi.org/10.1061/JCCEE5.CPENG-6106 Citation Details
Park, Somin and Menassa, Carol C and Kamat, Vineet R "Joint BERT Model for Intent Classification and Slot Filling Analysis of Natural Language Instructions in Co-Robotic Field Construction Work" , 2024 https://doi.org/10.1061/9780784485224.055 Citation Details
Park, Somin and Wang, Xi and Menassa, Carol C and Kamat, Vineet R and Chai, Joyce Y "Natural language instructions for intuitive human interaction with robotic assistants in field construction work" Automation in Construction , v.161 , 2024 https://doi.org/10.1016/j.autcon.2024.105345 Citation Details
Ruan, Daniel and Mcgee, Wes and Adel, Arash "Reducing Uncertainty in Multi-Robot Construction through Perception Modelling and Adaptive Fabrication" , 2023 https://doi.org/10.22260/isarc2023/0006 Citation Details
(Showing: 1 - 10 of 23)

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