BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//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:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220318T103000
DTEND;TZID=America/New_York:20220318T114500
DTSTAMP:20260406T033014
CREATED:20220304T180229Z
LAST-MODIFIED:20220304T180229Z
UID:6480-1647599400-1647603900@seasevents.nmsdev7.com
SUMMARY:GRASP on Robotics: Gregory Hager\, Johns Hopkins University\, “Observing\, Learning and Executing Fine-Grained Manipulation Activities”
DESCRIPTION:This seminar will be held in person in Wu and Chen Auditorium as well as virtually via Zoom. \nIn the domain of image and video analysis\, much of the deep learning revolution has been focused on narrow\, high-level classification tasks that are defined through carefully curated\, retrospective data sets. However\, most real-world applications – particularly those involving complex\, multi-step manipulation activities — occur “in the wild” where there is a combinatorial long tail of unique situations that are never seen during training. These systems demand a richer\, fine-grained task representation that is informed by the application context and which supports quantitative analysis and compositional synthesis. As a result\, the challenges inherent in both high-accuracy\, fine-grained analysis and performance of perception-based activities are manifold\, spanning representation\, recognition\, and task and motion planning. \n  \nThis talk will summarize our work addressing these challenges. I’ll first describe DASZL\, our approach to interpretable\, attribute-based activity detection. DASZL operates in both pre-trained and zero shot settings\, and it has been applied to a variety of applications ranging from surveillance to surgery. I will then describe our recent work on “Good Robot”\, a method for end-to-end training of a robot manipulation system. Good Robot achieves state-of-the-art performance in complex\, multi-step manipulation tasks\, and we show it can be refactored to support both demonstration-driven and language-guided manipulation. I’ll close with a summary of some directions related to these technologies that we are currently exploring.
URL:https://seasevents.nmsdev7.com/event/grasp-on-robotics-gregory-hager-johns-hopkins-university-observing-learning-and-executing-fine-grained-manipulation-activities/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
END:VCALENDAR