Portrait of Liam Paull

Liam Paull

Core Academic Member
paulll@mila.quebec
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Computer Vision
Deep Learning
Robotics
Website
Google Scholar
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Biography

Liam Paull is an associate professor at Université de Montréal and co-leads the Montréal Robotics and Embodied AI Lab (REAL). His lab focuses on a variety of robotics problems, including building representations of the world for such applications as simultaneous localization and mapping, modelling uncertainty, and building better workflows to teach robotic agents new tasks through, for example, simulation or demonstration.

Previously, Paull was a research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT), where he led the autonomous car project funded by the Toyota Research Institute (TRI). He completed a postdoc with the Marine Robotics Group at MIT, where he worked on Simultaneous Localization and Mapping (SLAM) for underwater robots.

His PhD from the University of New Brunswick in 2013 focused on robust and adaptive planning for underwater vehicles. He is also the co-founder and director of the Duckietown Foundation, which is dedicated to making engaging robotics learning experiences accessible to everyone.

Current Students

Francesco Argenziano
Independent visiting researcher - Sapienza
Google Scholar
Ria Arora
Master's Research - Université de Montréal
Principal supervisor :
Guy Wolf
Github
Adam Burhan
Master's Research - Université de Montréal
Github
Rodrigue De Schaetzen
PhD - Université de Montréal
Website
Github
Google Scholar
Manfred Diaz Cabrera
PhD - Université de Montréal
Website
Github
Google Scholar
Moustafa Elarabi
PhD - Université de Montréal
Website
Github
Google Scholar
Charlie Gauthier
PhD - Université de Montréal
Co-supervisor :
Glen Berseth
Anshul Gupta
Collaborating researcher - Université de Montréal
Co-supervisor :
Sarath Chandar
Website
Zhen Liu
Collaborating Alumni - Université de Montréal
Co-supervisor :
Yoshua Bengio
Kaustubh Mani
PhD - Université de Montréal
Github
Google Scholar
Sacha Morin
PhD - Université de Montréal
Principal supervisor :
Guy Wolf
Website
Github
Google Scholar
Yann Pequignot
Collaborating researcher - Université Laval
Google Scholar
Azalee Robitaille
Master's Research - Université de Montréal
azalee.robitaille@hotmail.com
Luke Rowe
PhD - Université de Montréal
Co-supervisor :
Chris Pal
Website
Github
Google Scholar
Miguel Angel Saavedra Ruiz
PhD - Université de Montréal
miguel-angel.saavedra-ruiz@mila.quebec
Website
Github
Google Scholar
Mahtab Sandhu
Master's Research - Université de Montréal
Github

Publications

Curriculum in Gradient-Based Meta-Reinforcement Learning
Bhairav Mehta
Tristan Deleu
Sharath Chandra Raparthy
Chris Pal
Liam Paull
Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML) have shown strong few-shot performance in supervised and reinforcem… (see more)ent learning settings. However, specifically in the case of meta-reinforcement learning (meta-RL), we can show that gradient-based meta-learners are sensitive to task distributions. With the wrong curriculum, agents suffer the effects of meta-overfitting, shallow adaptation, and adaptation instability. In this work, we begin by highlighting intriguing failure cases of gradient-based meta-RL and show that task distributions can wildly affect algorithmic outputs, stability, and performance. To address this problem, we leverage insights from recent literature on domain randomization and propose meta Active Domain Randomization (meta-ADR), which learns a curriculum of tasks for gradient-based meta-RL in a similar as ADR does for sim2real transfer. We show that this approach induces more stable policies on a variety of simulated locomotion and navigation tasks. We assess in- and out-of-distribution generalization and find that the learned task distributions, even in an unstructured task space, greatly improve the adaptation performance of MAML. Finally, we motivate the need for better benchmarking in meta-RL that prioritizes \textit{generalization} over single-task adaption performance.
2020-02-19
ArXiv (preprint)
arxiv.org
arxiv.org
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
Tong Che
Ruixiang ZHANG
Jascha Sohl-Dickstein
Hugo Larochelle
Liam Paull
Yuan Cao
Yoshua Bengio
We show that the sum of the implicit generator log-density …
arxiv.org
A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies
Homanga Bharadhwaj
Zihan Wang
Yoshua Bengio
Liam Paull
Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should n… (see more)ot require manual tuning or calibration. In the case of a robot operating in a real environment the training process can be costly, time-consuming, and even dangerous since failures are common at the start of training. For this reason, it is desirable to be able to leverage simulation and off-policy data to the extent possible to train the robot. In this work, we introduce a robust framework that plans in simulation and transfers well to the real environment. Our model incorporates a gradient-descent based planning module, which, given the initial image and goal image, encodes the images to a lower dimensional latent state and plans a trajectory to reach the goal. The model, consisting of the encoder and planner modules, is first trained through a meta-learning strategy in simulation. We subsequently perform adversarial domain transfer on the encoder by using a bank of unlabelled but random images from the simulation and real environments to enable the encoder to map images from the real and simulated environments to a similarly distributed latent representation. By fine tuning the entire model (encoder + planner) with only a few real world expert demonstrations, we show successful planning performances in different navigation tasks.
2019-05-20
2019 International Conference on Robotics and Automation (ICRA) (published)
doi.org
arxiv.org