BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.16.3//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:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251203T150000
DTEND;TZID=America/New_York:20251203T160000
DTSTAMP:20260601T175010
CREATED:20251125T195953Z
LAST-MODIFIED:20251125T195953Z
UID:15252-1764774000-1764777600@seasevents.nmsdev7.com
SUMMARY:Fall 2025 GRASP SFI: Minghan Zhu\, University of Pennsylvania\, "3D Robot Vision for Structured World Understanding"
DESCRIPTION:This presenter is one of the winners of the 2025 GRASP vote for internal PhD or postdoc SFI Speakers! \nThis is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom.  \nABSTRACT\nDeploying robots in diverse real-world environments is a fundamental challenge. While recent AI advances are impressive\, robots still struggle to generalize. I argue that a key missing piece in embodied intelligence is the structured understanding of the world: how geometries compose\, how physics governs interactions\, and how dynamics unfold. My research in 3D vision develops this understanding with two complementary principles: physics-based reasoning and symmetry-aware learning. First\, I present Vysics\, fusing vision and contact-rich physics to overcome heavy occlusions in object reconstruction\, and my recent follow-up work that further incorporates 3D generative priors for reconstructions with both high visual fidelity and physical compliance. Then\, I demonstrate my work on leveraging symmetry for efficient modeling of 3D geometry and dynamics. I introduce my algorithmic contributions in equivariant learning\, including E2PN\, which improves the efficiency of point cloud learning by 7x compared with prior work\, and Lie Neurons and Reductive Lie Neurons\, which expand the scope of symmetry preserved by equivariant networks from rotations to general linear transformations. These advances enable significant progress in various robotic tasks by incorporating symmetry\, from segmentation and place recognition to odometry and dynamics learning. I will close with my vision of building structured world representations that are simultaneously grounded in physics\, informed by data\, and structured by symmetry\, toward robots that truly understand their physical world.
URL:https://seasevents.nmsdev7.com/event/fall-2025-grasp-sfi-minghan-zhu/
LOCATION:Levine 307\, 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