Skip to Main Content

Robotics

U of T Engineering has the largest and most diverse robotics program in Canada, and together with a range of strategic industrial partners we are ushering in a future where robots will extend human capabilities and improve lives.

50+
researchers with robotics focus
Largest robotics research program in Canada
$45M+
in total research funding since 2010
  • Advanced Manufacturing
  • Aerial Robotics
  • Artificial Intelligence
  • Assistive Robotics
  • Autonomous Vehicles
  • Human Factors and Transportation
  • Machine Learning
  • Microrobotics
  • Nanorobotics
  • Personal Robotics
  • Rehabilitation
  • Robots for Society
  • Surgical Robotics

Centre for Aerial Robotics Research & Education

CARRE expands and unifies research and teaching activities related to the burgeoning field of aerial robotics.

Toronto Institute for Advanced Manufacturing

TIAM expedites research and development of advanced manufacturing technologies by creating a multidisciplinary network focused on sharing knowledge, ideas and resources.

Institute for Robotics & Mechatronics

IRM brings focus to research in robotics and mechatronics through collaborative research projects and innovative educational programs.

Study Robotics at U of T Engineering

Graduate students can choose from a wide range of technical emphases, including Robotics & Mechatronics and Advanced Manufacturing. Engineering undergraduates can complement their studies with minors in Robotics & Mechatronics, Advanced Manufacturing and Nanoengineering. Engineering Science students can major in Robotics as well as Machine Intelligence — the first program of its kind in Canada.

Leading innovation starts here

Connect with our partnerships team to discuss how a partnership with U of T Engineering can benefit your organization.

Learn more »

Give a gift

Support the future of robotics research.

Learn More

A team of researchers led by Professor Jonathan Kelly (UTIAS) has found a way to enhance the visual perception of robotic systems by coupling two different types of neural networks. The innovation could help autonomous vehicles navigate busy streets or enable medical robots to work effectively in crowded hospital hallways. 

“What tends to happen in our field is that when systems don't perform as expected, the designers make the networks bigger — they add more parameters,” says Kelly. 

“What we’ve done instead is to carefully study how the pieces should fit together