Trevor Ablett

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

For a full list of my published work, see my Google Scholar page. I am investigating methods for improving imitation learning algorithms for robotic manipulators, by making them more sample efficient and/or more applicable to the challenges of real robotic tasks.

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Tactile Imitation Learning

Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor
Trevor Ablett, Oliver Limoyo, Adam Sigal, Affan Jilani, Jonathan Kelly, Kaleem Siddiqi, Francois Hogan, Gregory Dudek
Submitted to IEEE Transactions on Robotics (T-RO): Special Section on Tactile Robotics, February 2024
 Blog   Preprint   Code 

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Learning to Place by Picking

Working Backwards: Learning to Place by Picking
Oliver Limoyo, Abhisek Konar, Trevor Ablett, Jonathan Kelly, Francois R. Hogan, Gregory Dudek
arXiv:2312.02352, December 2023
 Preprint 

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Learning from Guided Play

Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks
Trevor Ablett, Bryan Chan, Jonathan Kelly
In IEEE Robotics and Automation Letters (RA-L) presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'23), Detroit, MI, USA, Oct. 1-5, 2023
 Blog   Preprint   Code   Video 

Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning
Trevor Ablett*, Bryan Chan*, Jonathan Kelly (*equal contribution)
Accepted at the Neurips 2021 Deep Reinforcement Learning Workshop, Sydney, Australia, 13 Dec., 2021
 Preprint   Poster   Video 

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Multimodal Sequential Latent Variable Models

Learning Sequential Latent Variable Models from Multimodal Time Series Data
Oliver Limoyo, Trevor Ablett, Jonathan Kelly
Accepted to the International Conference on Intelligent Autonomous Systems (IAS'17), Zagreb, Croatia, June 13-16, 2022
Finalist for the Best Paper Award
 Preprint   Code 

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Multiview Manipulation

Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations
Trevor Ablett, Daniel (Yifan) Zhai, Jonathan Kelly
In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prauge, Czech Republic, 27 Sept. - 1 Oct. 2021
 Blog   Preprint   Code   Video 

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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 

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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 

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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 

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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