BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.19//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:20260408T185853
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