Trevor Ablett

Trevor Ablett

PhD candidate at University of Toronto in the STARS Lab.
Researching the intersection of machine learning, robotics, and pschology.

For a full list of my published work, see my Google Scholar page. I am investigating methods for improving imitation learning algorithms on robotic manipulators, specifically by taking inspiration from neuroscience and behavior. My previous research involved improving the autonomy of manipulators through novel approaches to self-calibration.


Intervention-based Learning

Fighting Failures with FIRE: Failure Identification to Reduce Expert Burden in Intervention-Based Learning
Trevor Ablett, Filip Maric, and Jonathan Kelly
Technical Report STARS-20-001, July 2020
 Preprint   Video 


Manipulability Optimization

Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization
Filip Maric, Oliver Limoyo, Luka Petrovic, Trevor Ablett, Ivan Petrovic and Jonathan Kelly
In Proceedings of the 2019 IEEE International Conference on Intelligent Robots and Systems (IROS), Macau, China, 4 - 8 November 2019
 Preprint   Video 


Contact Calibration

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


Patents

Method of Calibrating a Mobile Manipulator
Jonathan Kelly, Oliver Limoyo, Trevor Ablett
US Patent No. WO/2019/165561

Vision-based system for navigating a robot through an indoor space
Robert Peters, Chanh Vy Tran, Trevor Louis Ablett, Lucas James Lepore, Matthew James Sergenese
US Patent App. 14/886,698, 2017

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