Deep Neural Networks for Robotics
We aim to develop a platform-independent approach that utilizes deep neural networks (DNNs) to enhance classical controllers to achieve...
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...
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...
Robotic Swarms
There are tasks that cannot be done by a single robot alone. A group of robots collaborating on a task has the potential of being highly efficient,...
Efficient Multi-Task and Multi-Robot Learning
Can robots learn from each other? Is sharing information between robots beneficial? Robots should be able to learn from few demonstrations of a task,...
Aerial Robotics for Mining
In this project we aim to develop UAV based monitoring and data acquisition solutions that enable efficient and timely decision-making for...
Counter-Gust System for Hybrid Aerial Vehicles
The project objective of this project is to design and implement an automatic crosswind stabilization system for a new, hybrid aerial vehicle...
