Skip directly to content

Minimize RSR Award Detail

Research Spending & Results

Award Detail

Doing Business As Name:University of Michigan Ann Arbor
  • Joyce Y Chai
  • (734) 764-8505
Award Date:09/23/2019
Estimated Total Award Amount: $ 766,710
Funds Obligated to Date: $ 782,710
  • FY 2018=$766,710
  • FY 2020=$16,000
Start Date:07/15/2019
End Date:09/30/2022
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:NRI: INT: COLLAB: Collaborative Task Planning and Learning through Language Communication in a Human-Robot Team
Federal Award ID Number:1949634
DUNS ID:073133571
Parent DUNS ID:073133571
Program:NRI-National Robotics Initiati
Program Officer:
  • Tatiana Korelsky
  • (703) 292-8930

Awardee Location

Street:3003 South State St. Room 1062
City:Ann Arbor
County:Ann Arbor
Awardee Cong. District:12

Primary Place of Performance

Organization Name:University of Michigan Ann Arbor
Street:EECS department
City:Ann Arbor
County:Ann Arbor
Cong. District:12

Abstract at Time of Award

When deployed in the field, robots will often encounter new situations or new tasks they don't have any knowledge or experience about. Even given sufficient knowledge, designing planners that can generate high quality plans and perform efficiently across various domains remains an open challenge. To address these issues, this project aims to empower robots to harness human expertise to acquire new knowledge and to engage humans in the loop of plan generation so that humans and robots can collectively arrive at a joint plan. The results will lead to principles and computational models for enabling effective human-robot teams that can adapt to new and changing environments and tasks, which will benefit many applications such as manufacturing, service, assistive technology, and search and rescue. This project will also provide new exciting training and education opportunities for students through research mentoring and curriculum development. This project investigates how humans and robots strive to mediate goals, world models, and plans to establish common ground for joint tasks. It will develop a computational framework that tightly links language and dialogue processing with the robot's underlying planning system to support collaborative task planning and learning in a human-robot team. It further will evaluate collaborative model acquisition and plan generation in terms of consistency of shared understanding, plan quality, and situational awareness. The research will transform planning in a human-robot team by integrating human expertise and knowledge in a collaborative process to improve planning and task performance. It will endow the robot with an ability to explain its internal states, goals and plans, and to continuously learn new states, actions, and plans through language communication with human partners. It will also advance language and dialogue research by providing a rich context for studying grounded semantics of language. 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.

For specific questions or comments about this information including the NSF Project Outcomes Report, contact us.