Master of Engineering (MEng) emphasis:

Robotics

Questions?

If you have questions about the Robotics emphasis contact: robotics.gradstudies@utoronto.ca

Review the steps on how to apply for an emphasis in Robotics.

For more information, visit the MEng emphases FAQ page »

Robotics is a fast-changing, highly interdisciplinary field. From health-care to manufacturing and customer service, the design and operation of robots is reshaping society and reimagining industries across the globe.

The MEng emphasis in Robotics is part of the University of Toronto Robotics Institute, which is home to the largest and most diversified robotics research program in Canada. Students will have the opportunity to collaborate with leading robotics experts from across the university around three research pillars: autonomous field robotics, health-care robotics and advanced manufacturing.

Students who complete the requirements of the emphasis will have it notated on their transcript.

Program Eligiblity

Graduate students registered in the following MEng programs can specialize in the Robotics emphasis:

You cannot apply to the emphasis directly as a standalone program. If you are a prospective student, you can learn more about applying to our MEng programs here.  

Careers

Graduates who complete the Robotics emphasis are employed by companies in areas including:

aerospace,
automation,
finance,
health-care,
manufacturing,
retail,
telecom

and more.

Some of the first roles our graduates secure are:

Autonomy engineer
Data scientist
Hardware engineer
Machine learning developer
Process engineer
Robotics engineer
System engineer

Alumni Profiles

The MEng and the Robotics and Mechatronics emphasis strengthened my holistic understanding of core mechanical engineering concepts and their applications across other STEM fields, enabling me to build a career in the nuclear power and radiopharmaceutical industries. The projects, as part of several courses, were typically state-of-the-art industry problems that gave me professional experience in the academic setting.

Vedang Acharya, MEng graduate

Integrating advanced robotics concepts with hands-on experience has allowed me to work on cutting-edge projects and develop a deep understanding of robotic systems, from fundamental theories to complex applications. As practical experience is often what sets candidates apart in the job market, the challenging projects I completed throughout my MEng degree have become invaluable assets.

Yesim Barista, MEng graduate

Emphasis Requirements

To complete an emphasis in Robotics, eligible students must successfully complete four courses (2.0 full-course equivalents – or “FCEs”) chosen from at least two course groups, and no more than two in any given group. Note that not all courses will be offered each year.

Pre-approved substitutions are listed below. Each student will only be allowed one substitution.

Some courses may satisfy the requirements of multiple emphases. Students may double-count a maximum of one course towards the requirements of any two emphases.

Students cannot earn more than two emphases.

Courses

Course descriptions are published on the U of T Robotics Institute website.

Group 1 Planning and Control

AER1516H: Motion Planning for Robotics
AER1517H: Control for Robotics
ECE557H: Linear Control Theory (Exclusion: ECE410H)
ECE1619H: Linear Geometric Control Theory
ECE1658H: Geometric Nonlinear Control of Robotic Systems
ECE1636H: Control of Discrete-Event Systems I
ECE1647H: Introduction to Nonlinear Control Systems
ECE1653H: Hybrid Systems and Control Applications
ECE1657H: Game Theory and Evolutionary Games
MIE1064H: Control Methods with Applications To Robotics
MIE1068H: Nonlinear Control

Pre-approved Group 1 substitutions

ECE1635: Special Topics in Control I
ECE1505: Convex Optimization

Group 2: Perception and Learning 

AER1513H: State Estimation for Aerospace Vehicles
AER1515H: Perception for Robotics (Exclusion: ROB1514)
CSC2503H: Foundations of Computer Vision
CSC2506H: Probabilistic Reasoning
CSC2515H: Introduction to Machine Learning
CSC2541H: Topics in Machine Learning
CSC2548H: Machine Learning in Computer Vision
ECE516H: Intelligent Image Processing
ECE1511H: Signal Processing
ECE1512H: Digital Image Processing and Applications
JEB1433H: Medical Imaging
ROB501H: Computer Vision for Robotics

Pre-approved Group 2 substitutions

STA2104: Statistical Methods for Machine Learning II
ECE1513: Intro to Machine Learning (cannot substitute with CSC2515)
CSC2606: Continuum Robotics
MIE1077: Artificial Intelligence for Robotics III (AIR III): Advanced AI-Based Robot Applications
ECE1508H: Special Topics in Communications: Applied Deep Learning
ECE1508H: Special Topics in Communications: Reinforcement Learning