NSF Org:	IIS Division of Information & Intelligent Systems
Recipient:	OHIO STATE UNIVERSITY, THE
Initial Amendment Date:	February 16, 2023
Latest Amendment Date:	February 16, 2023
Award Number:	2238402
Award Instrument:	Continuing Grant
Program Manager:	Todd Leen tleen@nsf.gov  (703)292-7215 IIS  Division of Information & Intelligent Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	May 1, 2023
End Date:	April 30, 2028 (Estimated)
Total Intended Award Amount:	$554,558.00
Total Awarded Amount to Date:	$323,314.00
Funds Obligated to Date:	FY 2023 = $323,314.00
History of Investigator:	Martijn Ijtsma (Principal Investigator) ijtsma.1@osu.edu
Recipient Sponsored Research Office:	Ohio State University 1960 KENNY RD COLUMBUS OH  US  43210-1016 (614)688-8735
Sponsor Congressional District:	03
Primary Place of Performance:	Ohio State University 1960 KENNY RD COLUMBUS OH  US  43210-1016
Primary Place of Performance Congressional District:	03
Unique Entity Identifier (UEI):	DLWBSLWAJWR1
Parent UEI:	MN4MDDMN8529
NSF Program(s):	HCC-Human-Centered Computing
Primary Program Source:	01002324DB NSF RESEARCH & RELATED ACTIVIT 01002627DB NSF RESEARCH & RELATED ACTIVIT 01002728DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	1045, 7367
Program Element Code(s):	736700
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

The deployment of smart robots promises increased safety, productivity, and capability in domains such as disaster and emergency response, ground mobility, manufacturing, aviation, and space operations. Good human-robot collaboration is key to realizing these promises. This project develops novel modeling techniques for analyzing and designing collaborative behavior in human-robot teams. Collaborative behavior requires adjusting to and communicating with each other. Coordination and communication incur cognitive and temporal costs. In human-robot collaboration these costs can be high, as coordination with autonomous agents generally is more taxing and time-consuming than collaboration with other humans. The models developed in this project will identify the causes and effects of coordination costs in human-robot systems. Based on these models, the project develops techniques for managing coordination costs to avoid overloading human operators. Improved cost management will lead to more robust and resilient human-robot operations, broader adoption of smart robotic technologies, and realization of their promise. The project integrates the research with education and outreach activities to train the future workforce in systems thinking and interdisciplinary problem-solving skills. These skills will ready future engineers, researchers, and scientists to create integrated solutions to address complex challenges that span technological, human, ecological, economic, and policy dimensions.

This project develops a generalizable formalization for representing and analyzing joint activity in human-robot systems by combining theories from cognitive and social sciences with techniques from graph theory and agent-based modeling. This framework allows objective and dynamic analysis of the teamwork required to manage interdependencies between humans and robots. Based on the model, the research develops techniques for dynamically adapting and controlling coordination costs to improve collaboration and avoid lapses. The work will be validated in disaster response and space operations. The project addresses three fundamental research challenges: First, it determines the relation between a human-robot system organization, asymmetries in cooperative competencies, and cognitive and temporal costs of coordinating with robots. Second, it identifies control strategies for dynamically regulating coordination costs in human-robot systems. Third, it demonstrates the use of graph-theoretical metrics and algorithms to translate theoretical concepts of joint activity into actionable guidance for making robots more cooperative agents in dynamic environments. Findings will provide deep insight into what capabilities robots need to be endowed with to make them useful cooperative agents in context. These insights will tell us how robotic functionality should be deployed to improve the robustness and resilience of complex operations.

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.

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