News

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Learning from Reality Without Giving Up the Model

A hybrid approach helps autonomous systems adapt quickly by blending simple models with real-world experience.
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Recent External Grants

MnRI would like to highlight the following individuals.
Fang

Algorithm-Hardware Codesign for Real-Time Robotics

As robots become increasingly capable, they also become increasingly computationally demanding.
Mnri

August 2026 In the News

MnRI would like to highlight the following individuals.
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August 2026 Director Updates

The activities of the Minnesota Robotics Institute (MnRI) revolve around three areas: (a) research, (b) education, and (c) outreach — all of which are interconnected and equally important for MnRI’s
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Introduction to Intelligent Robotic Systems

A fun and exciting way to learn in the Introduction to Intelligent Robotic Systems course.
Yue Yu

Coordinated UAV-UGV Trajectory Optimization via Model-Algorithm Co-Design

Teaming up heterogeneous robots enables the combination of complementary capabilities that are difficult or impossible to achieve for a single robot.
ce yang

Modular Design and Development of Small-scale Robots for Agricultural Applications

Precision agriculture is reshaping how crops are managed by turning fields into data-rich, continuously monitored systems that can be managed plant by plant instead of field by field.
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MnRI Partners with Medtronic for Remote Stroke Treatment Research

Dr. Andy Grande is a professor of Neurosurgery at the University of Minnesota who routinely treats and oversees stroke cases in the Twin Cities. He turned to the College of Science and Engineering
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Guy and Huseynova Participate in Bell Museum Outreach Activity

Stephen Guy, Associate Professor in Computer Science and Engineering, and MnRI MS student Samra Huseynova participated in the Bell Museum Science Spotlight “Forest” event as part of an interactive
Mnri

February 2026 In the News

MnRI would like to highlight the following individuals.
MnRI logo over blurred image

February 2026 Director Updates

As we move into a landscape where federal funding for AI and quantum computing shifts from a single PI model to large multi-investigator efforts, we want to highlight the importance of working with