Melissa Greeff PhD

Assistant Professor

Electrical and Computer Engineering, Ingenuity Labs Research Institute, Faculty
Phone: 613-533-6562
Fax: 613-533-6489
Walter Light Hall, Room: 402

Expertise: Robotics and Control Systems, Vision-Based Navigation
Melissa Greeff
Biography Research Teaching Publications Melissa Greeff is an Assistant Professor in the department of Electrical and Computer Engineering. She is a faculty affiliate with the Vector Institute for Artificial Intelligence. Her research interests include aerial robots, vision-based navigation, and safe learning-based control. She obtained her BASc in Engineering Science and her PhD from the University of Toronto. For more information about her research, visit the Robora Lab webpage.

Research Affiliations:

Melissa works in the field of robotics. She is affiliated with the Vector Institute for Artificial Intelligence. For more information regarding her research projects, visit the Robora Lab webpage.

Research Area:

Aerial Robotics Control Systems Learning-Based Control Multi-Robot Systems Vision-Based Navigation Field Robotics

 MREN 223 Signals and Systems

Journals: 

Greeff, S. Zhou, and A. P. Schoellig, “Fly out the window: Exploiting discrete-time flatness for fast vision-based multirotor flight,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5023-5030, 2022. 

Brunke, M. Greeff, A. W. Hall, Z. Yuan, S. Zhou, J. Panerati, and A. P. Schoellig, “Safe learning in robotics: From learning-based control to safe reinforcement learning,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 5, pp. 411-444, 2022.

Yuan, A. W. Hall, S. Zhou, L. Brunke, M. Greeff, J. Panerati, A. P. Schoellig, ``safe-control-gym: A Unified Benchmark Suite for Safe Learning-Based Control and Reinforcement Learning,” IEEE Robotics and Automation Letters, Accepted. 

Greeff and A. P. Schoellig, “Exploiting differential flatness for robust learning-based tracking control using Gaussian Processes,” IEEE Control Systems Letters, vol. 5, no. 4, pp. 1121-1126, 2020. 

Warren, M. Greeff, B. Patel, J. Collier, A. P. Schoellig, and T. D. Barfoot, “There’s no place like home: Visual teach and repeat for emergency return of multirotor UAVs during GPS failure,” IEEE Robotics and Automation Letters, vol. 4, no. 1, pp. 161-168, 2019.  

 

Conferences: 

Greeff, A. W. Hall, and A. P. Schoellig, “Learning a stability filter for un- certain differentially flat systems using Gaussian Processes,” in Proc. IEEE Conference on Decision and Control (CDC), pp. 789-794, 2021. 

Greeff, T. D. Barfoot, and A. P. Schoellig, “A perception-aware flatness- based model predictive control for fast vision-based multirotor flight,” in Proc. IFAC World Congress, vol. 53, no. 2, pp. 9412-9419, 2020. 

Greeff and A. P. Schoellig, “Flatness-based model predictive control for quadrotor trajectory tracking,” in Proc. IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 6740-6745, 2018.  



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