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X-WR-CALNAME:Planetary Science and Exploration Seminar\, Daniele Gammelli: 
 "Advancing Aerospace Autonomy with Foundation Models"
X-WR-TIMEZONE:Pacific Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260608T234458Z
UID:tag:localist.com\,2008:EventInstance_52762654017797
DTSTART:20260506T193000Z
DTEND:20260506T202000Z
DESCRIPTION:Abstract: \n\nFoundation models\, trained on vast and diverse d
 ata that capture broad aspects of the human experience\, are at the heart 
 of the ongoing AI revolution\, transforming how we create\, problem-solve\
 , and work. These models—and the insights gained from developing them—
 are increasingly relevant to the advancement of autonomous robotic systems
 . In this talk\, I will highlight how recent efforts to apply foundation m
 odels to embodied systems can be leveraged to push the frontiers of aerosp
 ace autonomy. The discussion will focus on two main thrusts. First\, I wil
 l discuss how techniques traditionally developed in the foundation model l
 iterature can be adapted to enable reliable decision making in space\, wit
 h an emphasis on spacecraft rendezvous and proximity operations. Next\, I 
 will discuss the opportunities presented by foundation models within data-
 driven autonomy stacks\, ranging from automated data curation to serving a
 s high-level reasoning modules.\n \nBio: \n\nDr. Daniele Gammelli is a Pos
 tdoctoral Scholar in the Department of Aeronautics and Astronautics at Sta
 nford University. Since 2022\, he has been a Research Fellow at the Center
  for Aerospace Autonomy Research (CAESAR) at Stanford and\, until 2025\, a
 t the Center for Automotive Research at Stanford (CARS). He received his P
 h.D. in Machine Learning and Mathematical Optimization from the Technical 
 University of Denmark (DTU) in 2022\, where his doctoral thesis was nomina
 ted for the DTU Best Thesis of the Year Award and the DTU Young Researcher
  Award. More broadly\, Dr. Gammelli's research focuses on developing the a
 lgorithmic foundations and system-level methodologies that enable AI-power
 ed autonomous systems to operate safely\, efficiently\, and reliably in hi
 gh-stakes environments\, with an emphasis on aerospace autonomy and next-g
 eneration mobility systems.\n\n \n\n \n \nSuggested reading: \n\nSpeaker-s
 uggested readings: \nSpace-LLaVA: a Vision-Language Model Adapted to Extra
 terrestrial Applications. https://arxiv.org/abs/2408.05924\nLanguage-Condi
 tioned Safe Trajectory Generation for Spacecraft Rendezvous. https://seman
 tic-guidance4space.github.io/\nTransformer-based Model Predictive Control:
  Trajectory Optimization via Sequence Modeling. https://transformermpc.git
 hub.io/
GEO:37.426402;-122.172635
LOCATION:Mitchell Earth Sciences\, 350/372
SUMMARY:Planetary Science and Exploration Seminar\, Daniele Gammelli: "Adva
 ncing Aerospace Autonomy with Foundation Models"
URL;VALUE=URI:https://events.stanford.edu/event/planetary-science-and-explo
 ration-seminar-daniele-gammeli-advancing-aerospace-autonomy-with-foundatio
 n-models
CATEGORIES:Class/Seminar
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