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Award Abstract #1317718

NRI: Small: Understanding neuromuscular adaptations in human-robot physical interaction for adaptive robot co-workers

Div Of Information & Intelligent Systems
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Initial Amendment Date: September 3, 2013
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Latest Amendment Date: September 3, 2013
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Award Number: 1317718
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Award Instrument: Standard Grant
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Program Manager: Jeffrey Trinkle
IIS Div Of Information & Intelligent Systems
CSE Direct For Computer & Info Scie & Enginr
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Start Date: September 15, 2013
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End Date: August 31, 2017 (Estimated)
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Awarded Amount to Date: $1,199,427.00
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Investigator(s): Jun Ueda jun.ueda@me.gatech.edu (Principal Investigator)
Minoru Shinohara (Co-Principal Investigator)
Karen Feigh (Co-Principal Investigator)
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Sponsor: Georgia Tech Research Corporation
Office of Sponsored Programs
Atlanta, GA 30332-0420 (404)894-4819
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NSF Program(s): National Robotics Initiative
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Program Reference Code(s): 7923, 8086
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Program Element Code(s): 8013


The goal of this award is to develop theories, methods, and tools to understand the mechanisms of neuromotor adaptation in human-robot physical interaction. Human power-assisting systems, e.g., powered lifting devices that aid human operators in manipulating heavy or bulky loads, require physical contact between the operator and machine, creating a coupled dynamic system. This coupled dynamic has been shown to introduce inherent instabilities and performance degradation due to a change in human stiffness; when instability is encountered, a human operator often attempts to control the oscillation by stiffening their arm, which leads to a stiffer system with more instability. The project will establish control algorithms for robot co-workers that proactively adjust the contact impedance between the operator and robotic manipulator for achieving higher performance and stability. This research will 1) understand the association between neuromuscular adaptations and system performance limits, 2) develop probabilistic methods to classify and predict the transition of operator's cognitive and physical states from physiological measures, and 3) integrate this knowledge into a structure of shared human-robot and demonstrate the efficacy in a powered lifting device with real-world constraints at vehicle assembly facilities.

If successful, the research will benefit the communities interested in the adaptive shared control approach for advanced manufacturing and process design, including automobile, aerospace, and military. Such next-generation manufacturing is expected to improve productivity and reduce assembly time as well as physical burden of assembly line workers. Research outcomes will be integrated into current courses at both graduate and undergraduate levels. Students will be recruited from interdisciplinary and multicultural groups including under-represented groups. K-12 outreach will be carried out in conjunction with Georgia Tech Student and Teacher Enhancement Partnership Program and a summer robot camp in a local non-profit association. An online portal is maintained for dissemination.


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William Gallagher, Dalong Gao, Jun Ueda. "Improved stability of haptic human?robot interfaces using measurement of human arm stiffness," Advanced Robotics, v.28, 2014. 


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