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Adam Jacoff is a robotics research engineer in the U.S. Department of Commerce, National Institute of Standards and Technology (NIST), Engineering Laboratory, Intelligent Systems Division.
Over the past twenty years, he has developed a variety of innovative robots and directed evaluations of more than a hundred others in a range of sizes, including the first technology readiness level assessment of autonomous mobility for the U.S. Army's Experimental Unmanned Vehicle (XUV) (2002-2003).
His current efforts are focused toward developing a suite of 50 standard test methods for response robots to objectively evaluate ground, aquatic, and aerial system capabilities and facilitate integration of emerging technologies.
He has conducted dozens of international robot competitions using the test methods as challenge tasks to guide innovation and measure progress (2000-present). He has conducted dozens of robot exercises to refine and validate the test methods with emergency responders and robot manufacturers (2005-present). And he has conducted dozens of comprehensive robot evaluations using the test methods to quantify key capabilities guiding more than $60M of purchasing decisions for civilian and military organizations (2010-present). He is now validating use of the test methods as repeatable practice tasks to focus operator training and measure proficiency.
Recognition of his work includes: Three U.S. Department of Commerce Bronze Medal Awards (2002, 2004, 2011); the Federal Laboratory Consortium Award for Excellence in Technology Transfer (2008); NIST’s Applied Research Award (2006) and Measurement Science Award (2014); Commendations from the U.S. Departments of the Army (2009) and State (2014); and International Awards from the Japanese Society of Instrument and Control Engineers (2003), the International Rescue Systems Institute (2011), and the ASTM International Standards Society Award of Merit for lifetime achievement with honorary title of Fellow (2015).
He received a B.S. degree in Mechanical Engineering from the University of Maryland and a M.S. degree in Computer Science from Johns Hopkins University.
Intelligent Systems Division
Machine Systems Group
B.S. degree in Mechanical Engineering from the University of Maryland.
M.S. degree in Computer Science from Johns Hopkins University.