Oliver Limoyo

Ph.D. student at the STARS Lab, University of Toronto

Hi, I'm Oliver. My research focuses on the application of reinforcement learning for autonomous robots. I'm supervised by Prof. Jonathan Kelly.

Research

The Canadian Planetary Emulation Terrain Energy-Aware Rover Navigation Dataset

Olivier Lamarre, Oliver Limoyo, Filip Marić, and Jonathan Kelly

The International Journal of Robotics Research, 2019, manuscript # IJR-19-3688 (Submitted)

Reproducing Kindred’s UR-Reacher-2 Reinforcement Learning Experiment

I was one of the early testers of SenseAct, an open-source framework for real-time reinforcement learning. I confirmed the reproducibility of some of the learning experiments on our lab's robot, provided suggestions and improvements, and contributed to the repository on GitHub. A summary of my work is available in the blog post linked below.

Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization

Filip Marić, Oliver Limoyo, Luka Petrović, Trevor Ablett, Ivan Petrović, and Jonathan Kelly

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 4 - 8 November 2019

Manipulability Maximization Using Continuous-Time Gaussian Processes

Filip Marić, Oliver Limoyo, Luka Petrović, Ivan Petrović, and Jonathan Kelly

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Workshop, "Towards Robots that Exhibit Manipulation Intelligence", Madrid, Spain, 2018

Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Based Interaction

Oliver Limoyo, Trevor Ablett, Filip Maric, Luke Volpatti and Jonathan Kelly

Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 21 - 25 May 2018

Teaching Assistant

ROB501: Computer Vision for Robotics

Fall 2017, Fall 2018, Fall 2019

This course provides an introduction to aspects of computer vision specifically relevant to robotics applications. I was in charge of automating the grading of the programming assignments.

AER521: Mobile Robotics and Perception

Winter 2018

This course addresses the integration of state estimation, computer vision, control and planning for mobile robotics. I led the weekly lab sessions where the students implemented various algorithms on real hardware.

Projects

McGill Robotics: Autonmomous Underwater Vehicle Team

2015-2016

I was part of McGill Robotics, a student design team. I led the design and manufacturing of the pressure vessels for the batteries and hydrophones. I contributed to the refactoring, testing, and documentation of the main control code base.