Ground/Self-driving

Learning Control Theory and Foundations

Learning algorithms hold great promise for improving a robot's performance whenever a-priori models are not sufficiently accurate. We have developed...

Self-Driving Cars

As part of the SAE Autodrive Challenge, students in our lab will be working on designing, developing, and testing a self-driving car over the next...

High-Performance Robot Control and Planning

This project explores advanced control and planning algorithms, and their applicability to robotics problems. To achieve reliable robot operations...

Safe and Robust Robot Learning in Unknown Environments

Learning can be used to improve the performance of a robotic system in a complex environment. However, providing safety guarantees during the...

Vision-Based Flying and Driving

We use vision to achieve robot localization and navigation without using external infrastructure. Our ground robot experiments localize based on 3D...

Aerial and Ground Robot Racing

This project explores the physical limits of ground and aerial robots. When operating robots in these regimes, unknown dynamic effects (for example,...

University of Toronto Institute for Aerospace Studies