BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Laboratory for Computational Sensing + Robotics X-WR-CALDESC: X-FROM-URL:https://lcsr.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-13532@lcsr.jhu.edu DTSTAMP:20240328T182255Z CATEGORIES: CONTACT:Christy Brooks\; cbrook53@jhu.edu DESCRIPTION: \nLink for Live Seminar\nLink for Recorded seminars – 2022/202 3 school year\n \nAbstract:\nAll models are wrong\, and too many are direc ted inward. The Internal Model Principle of control engineering directs ou r attention (and modeling proficiency) to what makes the world around us p atterned and predictable. It says that driving a model of that patterned or predictable behavior in a feedback loop is the only way to achieve perf ect tracking or disturbance rejection. In the spirit of “some models are u seful”\, I will present a control system model of humans tracking moving t argets on a screen using a mouse and cursor. Simple analyses reveal this c ontroller’s robustness to visual blanking and experiments (even experiment s conducted remotely during the pandemic) provide ample support. Extension s that combine feedforward and feedback control complete the picture and c omplement existing literature in human motor behavior\, most of which is f ocused on modeling the system under control rather than the environment.\n Bio:\nBrent Gillespie is a Professor of Mechanical Engineering and Robotic s at the University of Michigan. He received a Bachelor of Science in Mech anical Engineering from the University of California Davis in 1986\, a Mas ter of Music from the San Francisco Conservatory of Music in 1989\, and a Ph.D. in Mechanical Engineering from Stanford University in 1996. His rese arch interests include haptic interface\, human motor behavior\, haptic sh ared control\, and robot-assisted rehabilitation after neurological injury . Prof. Gillespie’s awards include the Popular Science Invention Award (20 16)\, the University of Michigan Provost’s Teaching Innovation Prize (2012 )\, and the Presidential Early Career Award for Scientists and Engineers ( 2001).\n DTSTART;TZID=America/New_York:20230215T120000 DTEND;TZID=America/New_York:20230215T130000 LOCATION:Hackerman B17 SEQUENCE:0 SUMMARY:LCSR Seminar: Brent Gillespie “Predicting Human Behavior in Predict able Environments Using the Internal Model Principle” URL:https://lcsr.jhu.edu/events/lcsr-seminar-brent-gillespie-2/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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Abstract:
\nAll models are wrong\, and too many ar e directed inward. The Internal Model Principle of control engineering dir ects our attention (and modeling proficiency) to what makes the world arou nd us patterned and predictable. It says that driving a model of that pat terned or predictable behavior in a feedback loop is the only way to achie ve perfect tracking or disturbance rejection. In the spirit of “some model s are useful”\, I will present a control system model of humans tracking m oving targets on a screen using a mouse and cursor. Simple analyses reveal this controller’s robustness to visual blanking and experiments (even exp eriments conducted remotely during the pandemic) provide ample support. Ex tensions that combine feedforward and feedback control complete the pictur e and complement existing literature in human motor behavior\, most of whi ch is focused on modeling the system under control rather than the environ ment.
\nBio:
\nBrent Gillespie is a Professor of Mechanical En gineering and Robotics at the University of Michigan. He received a Bachel or of Science in Mechanical Engineering from the University of California Davis in 1986\, a Master of Music from the San Francisco Conservatory of M usic in 1989\, and a Ph.D. in Mechanical Engineering from Stanford Univers ity in 1996. His research interests include haptic interface\, human motor behavior\, haptic shared control\, and robot-assisted rehabilitation afte r neurological injury. Prof. Gillespie’s awards include the Popular Scienc e Invention Award (2016)\, the University of Michigan Provost’s Teaching I nnovation Prize (2012)\, and the Presidential Early Career Award for Scien tists and Engineers (2001).
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