Portrait de Liam Paull

Liam Paull

Membre académique principal
paulll@mila.quebec
Chaire en IA Canada-CIFAR
Professeur adjoint, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage profond
Robotique
Vision par ordinateur
Site web
Google Scholar
LinkedIn
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Biographie

Liam Paull est professeur adjoint à l'Université de Montréal et codirige le Laboratoire de robotique et d’IA intégrative de Montréal (REAL). Son laboratoire se concentre sur les problèmes de robotique, y compris la construction de représentations du monde (pour la localisation et la cartographie simultanées, par exemple), la modélisation de l'incertitude et la construction de meilleurs flux de travail pour enseigner de nouvelles tâches aux agents robotiques (notamment par la simulation ou la démonstration). Auparavant, Liam Paull a été chercheur au Computer Science and Artificial Intelligence Laboratory (CSAIL) du Massachusetts Institute of Technology (MIT), où il a dirigé le projet de voiture autonome financé par le Toyota Research Institute (TRI). Il a également été chercheur postdoctoral au laboratoire de robotique marine du MIT, où il a travaillé sur la technique SLAM (Simultaneous Localization and Mapping) pour les robots sous-marins. Il a obtenu son doctorat en 2013 à l'Université du Nouveau-Brunswick : il s’y est intéressé à la planification robuste et adaptative pour les véhicules sous-marins. Il est cofondateur et directeur de la Fondation Duckietown, dont l'objectif est de rendre accessibles à tous·tes les expériences d'apprentissage de la robotique.

Étudiants actuels

Francesco Argenziano
Visiteur de recherche indépendant - Sapienza
Google Scholar
Ria Arora
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Guy Wolf
Github
Adam Burhan
Maîtrise recherche - UdeM
Github
Rodrigue De Schaetzen
Doctorat - UdeM
Site web
Github
Google Scholar
Manfred Diaz Cabrera
Doctorat - UdeM
Site web
Github
Google Scholar
Moustafa Elarabi
Doctorat - UdeM
Site web
Github
Google Scholar
Charlie Gauthier
Doctorat - UdeM
Co-superviseur⋅e :
Glen Berseth
Anshul Gupta
Collaborateur·rice de recherche - UdeM
Co-superviseur⋅e :
Sarath Chandar
Site web
Zhen Liu
Collaborateur·rice alumni - UdeM
Co-superviseur⋅e :
Yoshua Bengio
Kaustubh Mani
Doctorat - UdeM
Github
Google Scholar
Sacha Morin
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guy Wolf
Site web
Github
Google Scholar
Samer Nashed
Postdoctorat - UdeM
Yann Pequignot
Collaborateur·rice de recherche - Université Laval
Google Scholar
Azalee Robitaille
Maîtrise recherche - UdeM
azalee.robitaille@hotmail.com
Luke Rowe
Doctorat - UdeM
Co-superviseur⋅e :
Chris Pal
Site web
Github
Google Scholar
Miguel Angel Saavedra Ruiz
Doctorat - UdeM
miguel-angel.saavedra-ruiz@mila.quebec
Site web
Github
Google Scholar
Mahtab Sandhu
Maîtrise recherche - UdeM
Github

Publications

Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers
Miguel Saavedra-Ruiz
Sacha Morin
Liam Paull
In this work, we consider the problem of learning a perception model for monocular robot navigation using few annotated images. Using a Visi… (voir plus)on Transformer (ViT) pretrained with a label-free self-supervised method, we successfully train a coarse image segmentation model for the Duckietown environment using 70 training images. Our model performs coarse image segmentation at the
2022-03-07
ArXiv (preprint)
doi.org
arxiv.org
Lifelong Topological Visual Navigation
Rey Reza Wiyatno
Anqi Xu
Liam Paull
Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space thro… (voir plus)ugh a topological map. Nevertheless, spurious or missing edges in the topological graph often lead to navigation failure. In this work, we propose a sampling-based graph building method, which results in sparser graphs yet with higher navigation performance compared to baseline methods. We also propose graph maintenance strategies that eliminate spurious edges and expand the graph as needed, which improves lifelong navigation performance. Unlike controllers that learn from fixed training environments, we show that our model can be fine-tuned using only a small number of collected trajectory images from a real-world environment where the agent is deployed. We demonstrate successful navigation after fine-tuning on real-world environments, and notably show significant navigation improvements over time by applying our lifelong graph maintenance strategies.
2021-10-16
ArXiv (preprint)
doi.org
arxiv.org
Perceptual Generative Autoencoders
Zijun Zhang
Ruixiang ZHANG
Zongpeng Li
Yoshua Bengio
Liam Paull
Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimension of dat… (voir plus)a can be much lower than the ambient dimension. We argue that this discrepancy may contribute to the difficulties in training generative models. We therefore propose to map both the generated and target distributions to a latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space. Specifically, we enforce the consistency in both the data space and the latent space with theoretically justified data and latent reconstruction losses. The resulting generative model, which we call a perceptual generative autoencoder (PGA), is then trained with a maximum likelihood or variational autoencoder (VAE) objective. With maximum likelihood, PGAs generalize the idea of reversible generative models to unrestricted neural network architectures and arbitrary number of latent dimensions. When combined with VAEs, PGAs substantially improve over the baseline VAEs in terms of sample quality. Compared to other autoencoder-based generative models using simple priors, PGAs achieve state-of-the-art FID scores on CIFAR-10 and CelebA.
2020-11-21
Proceedings of the 37th International Conference on Machine Learning (publié)
proceedings.mlr.press
openreview.net
Active Domain Randomization
Bhairav Mehta
Manfred Diaz
Florian Golemo
Chris Pal
Liam Paull
Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unkno… (voir plus)wn or cannot easily be used for training. In this work, we empirically examine the effects of domain randomization on agent generalization. Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters. We propose Active Domain Randomization, a novel algorithm that learns a parameter sampling strategy. Our method looks for the most informative environment variations within the given randomization ranges by leveraging the discrepancies of policy rollouts in randomized and reference environment instances. We find that training more frequently on these instances leads to better overall agent generalization. In addition, when domain randomization and policy transfer fail, Active Domain Randomization offers more insight into the deficiencies of both the chosen parameter ranges and the learned policy, allowing for more focused debugging. Our experiments across various physics-based simulated and a real-robot task show that this enhancement leads to more robust, consistent policies.
2020-05-12
Proceedings of the Conference on Robot Learning (publié)
proceedings.mlr.press
arxiv.org
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… (voir plus)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 (prépublication)
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… (voir plus)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) (publié)
doi.org
arxiv.org