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DTSTART;TZID=America/New_York:20190610T103000
DTEND;TZID=America/New_York:20190610T123000
DTSTAMP:20260408T230113
CREATED:20190530T153821Z
LAST-MODIFIED:20190530T153821Z
UID:10006229-1560162600-1560169800@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Improving Performance of Infiltrated SOFC Cathodes via Scaffold Engineering and Catalyst Surface Engineering"
DESCRIPTION:Committee Members: Raymond J. Gorte and John M. Vohs\, Co-Advisors; Aleksandra Vojvodic and Donald Berry.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-improving-performance-of-infiltrated-sofc-cathodes-via-scaffold-engineering-and-catalyst-surface-engineering/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
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DTSTART;TZID=America/New_York:20190610T110000
DTEND;TZID=America/New_York:20190610T120000
DTSTAMP:20260408T230113
CREATED:20190604T160021Z
LAST-MODIFIED:20190604T160021Z
UID:10006230-1560164400-1560168000@seasevents.nmsdev7.com
SUMMARY:NSF CAREER Awards Workshop
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/nsf-career-awards-workshop/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Panel Discussion
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DTSTART;TZID=America/New_York:20190611T103000
DTEND;TZID=America/New_York:20190611T120000
DTSTAMP:20260408T230113
CREATED:20190607T135743Z
LAST-MODIFIED:20190607T135743Z
UID:10006232-1560249000-1560254400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Hierarchical Task-Parameterized Learning from Demonstration "
DESCRIPTION:Many modern humanoid robots are designed to operate in human environments\, like homes and hospitals. Such robots could help humans accomplish tasks and lower their physical and/or mental workload. However\, robot users in homes and hospitals typically are not familiar with robotics or programming\, therefore it is difficult for them to adapt robots to their specific needs and environments. To remedy this situation\, many researchers turn to learning from demonstration (LfD)\, which enables a robot to emulate natural human movement as opposed to having an operator devise control policies and reprogram the robot for every new situation it encounters. \nWe suggest a hierarchical LfD structure of task-parameterized models\, particularly for object movement tasks that are ubiquitous in everyday life and could benefit from robotic support. Inspired by the task-parameterized Gaussian mixture model (TP-GMM) algorithm\, we develop the hierarchical structure and explicitly utilize task parameters to maximize the expected performance in a new situation from a few demonstrated situations. The robot can thus determine when it should request new demonstrations when the expected performance is too low. Other advantages of our approach include that a wider range of task situations can be modeled in the same framework without deteriorating performance and that adding or removing demonstrations incurs low computational load\, and thus the robot’s skill library can be built incrementally. We show these advantages in a simulated task and in the real world where naïve participants collaborated with a Willow Garage PR2 robot to move a handheld object. For most tested scenarios our hierarchical method achieved significantly better task performance and subjective ratings than both a passive model with only gravity compensation and a single TP-GMM encoding all demonstrations.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-hierarchical-task-parameterized-learning-from-demonstration/
LOCATION:Moore 216\, 200 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
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