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X-WR-CALNAME:Robotics Institute Carnegie Mellon University
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X-WR-CALDESC:Events for Robotics Institute Carnegie Mellon University
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DTSTART;TZID=America/New_York:20261009T113000
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SUMMARY:RI PhD Speaking Qual - Ryan Schuerkamp
DESCRIPTION:Date: Friday\, October 9\nTime: 11:30 am – 12:30 pm\nRoom location: GHC 4405\nZoom link: https://cmu.zoom.us/j/9873588831\n\nTitle: AdaSoS: Escaping the Exponential Cost of Higher-Order Sum-of-Squares Relaxations\n\nAbstract:\nMany problems in robotics\, power systems\, and machine learning are nonconvex polynomial optimization problems; local solvers return an answer but cannot tell you how far it is from the best one. The Sum-of-Squares (SoS) hierarchy fixes this with a sequence of convex semidefinite relaxations that give certified lower bounds and\, at high enough degree\, the global optimum. The catch is cost. Each step up the hierarchy grows the relaxation exponentially\, which puts higher-order SoS out of reach for real problems such as AC optimal power flow (AC-OPF)\, the problem of scheduling generators on the electric grid.\n\nThis talk presents Adaptive-SoS (AdaSoS)\, which gets the tightness of a higher-order relaxation without building it. AdaSoS checks whether the current low-order solution could extend to a valid higher-degree one. When it cannot\, the failure itself identifies which polynomial directions are missing\, and AdaSoS adds only those. We prove that AdaSoS reaches the full higher-order bound in finitely many steps. On our largest AC-OPF relaxation\, it certifies the global optimum 13× faster than the full higher-order relaxation\, with a 77% smaller semidefinite block. At the highest degree we solve\, it matches or improves on the bounds of existing approaches such as TSSOS\, CS-TSSOS\, and the adaptive hierarchy of Josz and Molzahn\, typically with a much smaller semidefinite block. AdaSoS can also start from cheaper sparse relaxations\, which lets it scale: on the IEEE 300-bus system\, where the dense higher-order relaxation does not fit in memory\, AdaSoS started from a sparse degree-1 relaxation tightens its bound to within 0.14% of the best known AC-OPF solution.\n\nCommittee members:\nDr. Geoff Gordon (advisor)\nDr. Drew Bagnell\nDr. Changliu Liu\nXinyu Li
URL:https://www.ri.cmu.edu/event/ri-phd-speaking-qual-ryan-schuerkamp/
LOCATION:Gates Hillman Center 4405
CATEGORIES:PhD Speaking Qualifier,Student Talks
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DTSTART;TZID=America/New_York:20261009T133000
DTEND;TZID=America/New_York:20261009T143000
DTSTAMP:20261009T081815
CREATED:20260924T142654Z
LAST-MODIFIED:20260924T142654Z
UID:153809-1791552600-1791556200@www.ri.cmu.edu
SUMMARY:RI Faculty Business Meeting
DESCRIPTION:Meeting for RI Faculty.\nIn person location – NSH 4305.\nZoom link available via calendar invite.
URL:https://www.ri.cmu.edu/event/ri-faculty-business-meeting-33-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:Faculty Events
ATTACH;FMTTYPE=image/png:https://www.ri.cmu.edu/app/uploads/2023/11/ri-new-mark-512-512-transparent.png
ORGANIZER;CN="RI Director's Office":MAILTO:lynnetta@cs.cmu.edu
END:VEVENT
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DTSTART;TZID=America/New_York:20261009T143000
DTEND;TZID=America/New_York:20261009T153000
DTSTAMP:20261009T081815
CREATED:20260828T165329Z
LAST-MODIFIED:20261002T191322Z
UID:153343-1791556200-1791559800@www.ri.cmu.edu
SUMMARY:How Can Robots Learn to Interact and Coordinate with Others?
DESCRIPTION:Abstract: Robots in the real world rarely act alone. They share spaces with other robots and with people\, and succeeding there means learning to interact and coordinate with them. This talk looks at what makes interaction hard and how we might get there. I start with multi-agent reinforcement learning\, where challenges like credit assignment make coordination difficult to learn\, and show how the reasoning capabilities of foundation models can help coach robots to learn coordination. Coordination doesn’t have to be learned through trial and error though\, so I then look at learning it from demonstrations. Imitating interacting agents turns out to be quite different from imitating a single one but it can still build on single-agent imitation. I then close by reversing the question: rather than asking what single-agent methods can do for multi-agent problems\, I ask what multi-agent thinking can do for a single robot and show that continual learning\, for example\, can be cast as a consensus optimization problem which results in a natural way to balance learning new tasks against retaining old ones.\n\nBio: Negar Mehr is an Assistant Professor of Mechanical Engineering at UC Berkeley\, where she directs the Berkeley Intelligent Control (ICON) Lab. Her research focuses on multi-agent interactions. Her lab develops learning-based methods grounded in game theory and control to enable robots to interact safely and intelligently with humans and other autonomous agents. She received her Ph.D. in Mechanical Engineering from UC Berkeley in 2019 and was a postdoctoral scholar in Aeronautics and Astronautics at Stanford University. Before returning to Berkeley\, she was an Assistant Professor of Aerospace Engineering at the University of Illinois Urbana-Champaign. She is a recipient of the ONR Young Investigator Award\, the NSF CAREER Award\, the ASME Rising Star Award\, and the IEEE ITSS Best Ph.D. Dissertation Award.
URL:https://www.ri.cmu.edu/event/ri-seminar-with-negar-mehr/
CATEGORIES:RI Seminar,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2026/08/negar-headshot-scaled-1.jpg
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