M.S. in Robotics Coursework

Take classes in several departments across the College of Science and Engineering, as part of the M.S. in Robotics program. We pair real-world issues with an educational setting and our industry collaboration allows students to connect to multiple outlets in Minnesota companies and beyond. 

Course Listing

This Minnesota Robotics Program is designed to be completed over the course of three semesters, for a total of 30 credits. One course will be required from each key area (for a total of 9 to 10 credits), selected from a short list of foundational courses for each area. The remaining credits will be for the capstone (3 to 6 credits) or thesis credits (10 credits), the Robotics Colloquium (1 credit), with the remaining credits coming from a list of elective courses from participating departments. 

Robotics Colloquium (1 credit) — Required

  • The Robotics Colloquium will be an introductory class, in which new students will be confronted with the basics of robotics and sensing, while getting an idea of the industry, opportunities, and how the Minnesota Robotics Institute's Master's program can work for them.

Key Areas (9 to 10 credits total) — Choose one (1) from each area

  • Cognition
    • Computer Science 5511 - Artificial Intelligence I (3 cr)
    • Computer Science 5512 - Artificial Intelligence II (3 cr)
    • Computer Science 5521 - Intro to Machine Learning (3 cr)
    • Computer Science 5521 - Machine Learning (3 cr)
  • Perception
    • Computer Science 5561 - Computer Vision (3 cr)
    • Electrical Engineering 5561 - Image Processing and Applications (3 cr)
  • Robot Modeling and Control:
    • Computer Science 5551 - Introduction to Intelligent Robotic Systems (3 cr)
    • Computer Science 5552 - Sensing/Estimation in Robotics (3 cr)
    • Mechanical Engineering 5286 - Robotics (4 cr, includes a lab)
    • Electrical Engineering 5231/Aerospace Engineering and Mechanics -  5321 Linear Systems and Optimal Control/Modern Feedback Control (3 cr)

Final Projects — Choose one (1), either the Master's Thesis OR the Capstone project

  • Master's Thesis (10 cr): Master’s theses (Plan A) will be supervised by a faculty advisor and follow the University rules. Students will have a committee of at least three faculty, need to write a thesis and defend it orally to their committee.  The Master’s thesis is especially useful for students who consider later transferring to a PhD degree or who are interested in a deeper research experience.
  • Capstone Project (3 - 6 cr): Capstone projects are provided by faculty and can also be provided in collaboration with industry partners. A capstone project will require at least one semester (3 cr for Plan B course) but can be extended to two semesters. Each student will be assigned a committee of 3 faculty members and will present their capstone project in a poster session in the Spring semester.  Students will have to write a report on their project that needs to be approved by the student’s committee.

Elective Courses (fill in credit total to 30 credits) — Choose up to six (6) additional classes

  • Aerospace Engineering and Mechanics
    • AEM 5333 - Design-to-Flight: Small Uninhabited Aerial Vehicles
    • AEM 5451 - Optimal Estimation
    • AEM 8411 - Advanced Dynamics
    • AEM 8421 - Robust Control (joint with EE)
    • AEM 8423 - Convex Optimization in Control
    • AEM 8495 - Advanced Topics Aerospace Systems (Topics course)
  • Electrical Engineering
    • EE 5235 - Robust Control System Design
    • EE 5239 - Introduction to Nonlinear Optimization
    • EE 5251 - Optimal Filtering and Estimation
    • EE-5542 - Adaptive Digital Signal Processing
    • EE 5561 - Image Processing and Applications
    • EE 5940 - Optimal Control and Reinforcement Learning
    • EE 5621/2 -  Physical Optics and Physical Optics Lab
    • EE 5624 -  Optical Electronics
    • EE 5705/7 - Electric Drives in Sustainable Energy Systems and Lab
    • EE 5391 - Computing with Neural Networks
    • EE 8215 - Nonlinear Systems
    • EE 8581 - Detection and Estimation Theory
    • EE 8591 - Predictive Learning from Data
    • EE 8231 - Optimization Theory
  • Mechanical Engineering
    • ME 5243 - Advanced Mechanism Design
    • ME 8283 - Smart Mechatronic Systems
    • ME 8285 - Advanced Control System Design, with Applications to Intelligent Vehicles
  • Computer Science
    • CSCI 5231 - Wireless and Sensor Networks
    • CSCI 5523 - Introduction to Data Mining
    • CSCI 5563 - Multiview 3D Geometry in Computer Vision
    • CSCI 5609 - Visualization
    • CSCI 5619 - Virtual Reality and 3D Interaction
    • CSCI 8980 - Special Advanced Topics in Computer Science (Topics course)

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Industry and Academic Collaboration

  • Courses are paused in the summertime, to allow students to participate in highly coveted and competitive internships and programs in industry. 
  • As mentioned under the benefits section, MnRI faculty is connected to key industries and top companies in the state of Minnesota and beyond. We are currently adding on-campus staff from Honeywell and there are vast research options in the state of Minnesota, which has large local presences from 3M, Amazon, Honeywell, Toro, General Mills, Boston Scientific, Medtronic, Land O'Lakes and much more.
  • MnRI enjoys many benefits of being connected to about three dozen faculty from across the University of Minnesota, however, the connections do not stop in the Twin Cities. We entertain almost weekly speakers from across the world to talk about their research and issues within the robotics industry and collaborate with many educational and governmental institutions.

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