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UID:10000048-1772722800-1772726400@csc.ncsu.edu
SUMMARY:Deployable Robots that Learn
DESCRIPTION:Abstract:\nWhile many robots are currently deployable in factories\, warehouses and homes\, their autonomous deployment requires either the deployment environments to be highly controlled\, or the deployment to only entail executing one single preprogrammed task. These deployable robots do not learn to address changes and to improve performance. For uncontrolled environments and for novel tasks\, current robots must seek help from highly skilled robot operators for teleoperated (not autonomous) deployment. \nIn this talk\, I will present three approaches to removing these limitations by learning to enable autonomous deployment in the context of mobile robot navigation\, a common core capability for deployable robots: \n(1) Interactive Learning by Adaptive Planner Parameter Learning fine-tunes existing motion planners by learning from simple interactions with non-expert users before autonomous deployment and adapts to different deployment scenarios; (2) In-Situ Learning of vehicle kinodynamics allows robots to learn from vehicle-terrain interactions during deployment and accurately\, quickly and stably navigate robots on unstructured off-road terrain; (3) Reflective Learning by Learning from Hallucination enables agile navigation in highly constrained deployment environments by reflecting on previous deployment experiences and creating synthetic obstacle configurations to learn from. In addition\, I will also briefly introduce a few more recent work on robot night vision\, social robot navigation\, multi-robot coordination and human-robot interaction. \nBio:\nXuesu Xiao is an Assistant Professor in the Department of Computer Science at George Mason University. Xuesu (Prof. XX) directs the RobotiXX lab\, in which researchers (XX-Men and XX-Women) and robots (XX-Bots) work together at the intersection of motion planning and machine learning with a specific focus on developing highly capable and intelligent mobile robots that are robustly deployable in the real world with minimal human supervision. \nXuesu’s work has been deployed in real-world robot field missions\, including search and rescue effort in the Mexico City earthquake and the Greece refugee crisis\, decommissioning effort in the Fukushima nuclear disaster and multiple search and rescue exercises in the US. Xuesu’s research has been featured by The New York Times\, WIRED\, US Army and IEEE Spectrum. Xuesu has been awarded State Council of Higher Education for Virginia (SCHEV) Outstanding Faculty Award (OFA) Rising Star\, the Commonwealth’s highest honor for faculty at Virginia’s public and private colleges and universities. \nHost:\nPeng Gao
URL:https://csc.ncsu.edu/event/deployable-robots-that-learn/
LOCATION:EB2 3001\, 890 Oval Drive\, Raleigh\, NC\, 27606\, United States
CATEGORIES:Lecture/Seminar
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DTSTART;TZID=America/New_York:20251017T110000
DTEND;TZID=America/New_York:20251017T120000
DTSTAMP:20260503T042227
CREATED:20251013T143113Z
LAST-MODIFIED:20251013T143537Z
UID:10000032-1760698800-1760702400@csc.ncsu.edu
SUMMARY:Behavior-Aware Data Valuation for LLMs at Scale
DESCRIPTION:Title: Behavior-Aware Data Valuation for LLMs at Scale \nAbstract:  \nLarge Language Models (LLMs) depend on massive datasets whose quality and influence remain largely opaque. Data valuation offers principled methods to quantify how training data contributes to model performance and behavior. Yet\, scaling classical approaches such as influence functions to trillion-token corpora continues to be a major challenge. This talk introduces recent advances that address this gap\, including the linearized influence kernel\, a new and efficient metric that extends to LLMs with billion-scale parameters. We will also highlight system-level frameworks such as RapidIn and present empirical findings of LLM training\, including the slowly change phenomenon\, which enables forward-looking valuation of future training data. By combining principled algorithms\, system optimizations\, and case studies\, the talk aims to bridge the gap between theory and practice. \nBio:  \nZhaozhuo Xu is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology. He received his Ph.D. from Rice University and an M.S. from Stanford University. His research develops randomized algorithms to enhance the efficiency of AI systems on commodity hardware. Dr. Xu’s work has appeared in leading venues such as NeurIPS\, ICML\, ICLR\, OSDI\, and ACL\, as well as in journals including Nature NPJ AI. His innovations in scalable AI have been integrated into widely used libraries like Hugging Face. He serves as an Associate Editor for Neurocomputing and as an Area Chair for major conferences\, including NeurIPS\, ICLR\, ICML\, ACL\, EMNLP\, NAACL\, and COLING. He is a recipient of the AAAI New Faculty Highlights (2025)\, the NSF CRII Award (2025)\, and the Stevens Bridging Award. \nClick here to stream the seminar.
URL:https://csc.ncsu.edu/event/behavior-aware-data-valuation-for-llms-at-scale/
LOCATION:EB2 3001\, 890 Oval Drive\, Raleigh\, NC\, 27606\, United States
CATEGORIES:CS AI Seminar Series,Lecture/Seminar
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