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BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Penn Engineering Events - ECPv6.18.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Penn Engineering Events X-ORIGINAL-URL:https://seasevents.nmsdev7.com X-WR-CALDESC:Events for Penn Engineering Events REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20180311T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20181104T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20190310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20191103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20200308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20201101T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20190618T103000 DTEND;TZID=America/New_York:20190618T120000 DTSTAMP:20190607T134252Z CREATED:20190607T134252Z LAST-MODIFIED:20190607T134252Z UID:1679-1560853800-1560859200@seasevents.nmsdev7.com SUMMARY:MEAM Seminar: "Machine Learning for Robotics: Achieving Safety\, Performance and Reliability by Combining Models and Data in a Closed-Loop System Architecture" DESCRIPTION:The ultimate promise of robotics is to design devices that can physically interact with the world. To date\, robots have been primarily deployed in highly structured and predictable environments. However\, we envision the next generation of robots (ranging from self-driving and -flying vehicles to robot assistants) to operate in unpredictable and generally unknown environments alongside humans. This challenges current robot algorithms\, which have been largely based on a-priori knowledge about the system and its environment. While research has shown that robots are able to learn new skills from experience and adapt to unknown situations\, these results have been limited to learning single tasks\, and demonstrated in simulation or lab settings. The next challenge is to enable robot learning in real-world application scenarios. This will require versatile\, data-efficient and online learning algorithms that guarantee safety when placed in a closed-loop system architecture. It will also require to answer the fundamental question of how to design learning architectures for dynamic and interactive agents. This talk will highlight our recent progress in combining learning methods with formal results from control theory. By combining models with data\, our algorithms achieve adaptation to changing conditions during long-term operation\, data-efficient multi-robot\, multi-task transfer learning\, and safe reinforcement learning. We demonstrate our algorithms in vision-based off-road driving and drone flight experiments\, as well as on mobile manipulators. URL:https://seasevents.nmsdev7.com/event/meam-seminar-machine-learning-for-robotics-achieving-safety-performance-and-reliability-by-combining-models-and-data-in-a-closed-loop-system-architecture/ LOCATION:Moore 216\, 200 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu END:VEVENT END:VCALENDAR