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-11871@lcsr.jhu.edu DTSTAMP:20240329T011407Z CATEGORIES: CONTACT:Ashley Moriarty\; amoriar2@jhu.edu DESCRIPTION:Link for Live Seminar\nLink for Recorded seminars – 2020/2021 s chool year\n \nAbstract:\nAutonomous systems offer the promise of providin g greater safety and access. However\, this positive impact will only be a chieved if the underlying algorithms that control such systems can be cert ified to behave robustly. This talk will describe a pair of techniques gro unded in infinite dimensional optimization to address this challenge.\nThe first technique\, which is called Reachability-based Trajectory Design\, constructs a parameterized representation of the forward reachable set\, w hich it then uses in concert with predictions to enable real-time\, certif ied\, collision checking. This approach\, which is guaranteed to generate not-at-fault behavior\, is demonstrated across a variety of different real -world platforms including ground vehicles\, manipulators\, and walking ro bots. The second technique is a modeling method that allows one to represe nt a nonlinear system as a linear system in the infinite-dimensional space of real-valued functions. By applying this modeling method\, one can empl oy well-understood linear model predictive control techniques to robustly control nonlinear systems. The utility of this approach is verified on a s oft robot control task.\n \nBiography:\nRam Vasudevan is an assistant prof essor in Mechanical Engineering and the Robotics Institute at the Universi ty of Michigan. He received a BS in Electrical Engineering and Computer Sc iences\, an MS degree in Electrical Engineering\, and a PhD in Electrical Engineering all from the University of California\, Berkeley. He is a reci pient of the NSF CAREER Award and the ONR Young Investigator Award. His wo rk has received best paper awards at the IEEE Conference on Robotics and A utomation\, the ASME Dynamics Systems and Controls Conference\, and IEEE O CEANS Conference and has been finalist for best paper at Robotics: Science and Systems.\n DTSTART;TZID=America/New_York:20210428T120000 DTEND;TZID=America/New_York:20210428T130000 LOCATION:https://wse.zoom.us/s/94623801186 SEQUENCE:0 SUMMARY:LCSR Seminar: Ram Vasudevan “How I Learned to Stop Worrying and Sta rt Loving Lifting to Infinite Dimensions” URL:https://lcsr.jhu.edu/events/ram-vasudevan/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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Abstract:
\nAutonomous systems offer the promise of providing greater safety and access. However\, this positive impact will only be achieved if the under lying algorithms that control such systems can be certified to behave robu stly. This talk will describe a pair of techniques grounded in infinite di mensional optimization to address this challenge.
\nThe first techni que\, which is called Reachability-based Trajectory Design\, constructs a parameterized representation of the forward reachable set\, which it then uses in concert with predictions to enable real-time\, certified\, collisi on checking. This approach\, which is guaranteed to generate not-at-fault behavior\, is demonstrated across a variety of different real-world platfo rms including ground vehicles\, manipulators\, and walking robots. The sec ond technique is a modeling method that allows one to represent a nonlinea r system as a linear system in the infinite-dimensional space of real-valu ed functions. By applying this modeling method\, one can employ well-under stood linear model predictive control techniques to robustly control nonli near systems. The utility of this approach is verified on a soft robot con trol task.
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Biography:
\nRam Vasud evan is an assistant professor in Mechanical Engineering and the Robotics Institute at the University of Michigan. He received a BS in Electrical En gineering and Computer Sciences\, an MS degree in Electrical Engineering\, and a PhD in Electrical Engineering all from the University of California \, Berkeley. He is a recipient of the NSF CAREER Award and the ONR Young I nvestigator Award. His work has received best paper awards at the IEEE Con ference on Robotics and Automation\, the ASME Dynamics Systems and Control s Conference\, and IEEE OCEANS Conference and has been finalist for best p aper at Robotics: Science and Systems.
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