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DTSTAMP:20241223T140733
DTSTART;TZID=America/Detroit:20250109T100000
DTEND;TZID=America/Detroit:20250109T110000
SUMMARY:Workshop / Seminar:MCDB Seminar> How do glia regulate synapse development and maintenance?
DESCRIPTION:Faculty Candidate
UID:130301-21865725@events.umich.edu
URL:https://events.umich.edu/event/130301
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Basic Science,Biology,Biosciences,Bsbsigns,Faculty Candidate,Natural Sciences,Research,Science
LOCATION:Biological Sciences Building - 1010
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20250106T132844
DTSTART;TZID=America/Detroit:20250109T100000
DTEND;TZID=America/Detroit:20250109T120000
SUMMARY:Presentation:Motion and Behavior Planning for Socially Assistive Robots
DESCRIPTION:Co-Chairs: Maani Ghaffari Jadidi and X. Jessie Yang \n\n\nAbstract:\nSocially Assistive Robots (SARs) help humans through social interactions. As robots are increasingly integrated into human spaces\, a key challenge is enabling them to navigate semi-structured environments. This thesis addresses robot behavior and motion planning in dynamic\, human-centric scenarios\, using a tour guide robot as a test case.\n\nThe first part focuses on tour planning with a shared map created collaboratively by Providers (managers) and Robots to guide Clients (visitors). The planner dynamically adapts routes based on constraints\, highlighting the importance of shared maps in human-robot tasks.\n\nThe second part explores low-level motion planning. We start with crowd navigation\, designing an agent to move through groups without disrupting the human flow. We then tackle narrow crossings\, using Smooth Maximum Entropy Deep Inverse Reinforcement Learning (S-MEDIRL)\, the robot learns from raw data to yield and avoid deadlock without relying on handcrafted heuristics (see attached gif). \n\nWe then evaluate Foundation Models in Socially Assistive settings\, demonstrating a robot as a greeter and tour guide at the University of Michigan Museum of Art (UMMA). The second part explores incorporating feedback from Vision and Language Models to distinguish between qualitatively good and bad social behaviors. We evaluate the efficacy of these generalized models in supervising and improving socially aware navigation\, specifically in the narrow crossing scenario.\n\nIn conclusion\, this thesis develops motion and behavior planning modules to enable socially aware robot navigation in human environments.\n\nZoom passcode: fetch
UID:130473-21866091@events.umich.edu
URL:https://events.umich.edu/event/130473
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Michigan Robotics,Robotics
LOCATION:Ford Robotics Building - 2300
CONTACT:
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