Liam Paull

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

Ria Arora

Master's Research - Université de Montréal

Principal supervisor :

Guy Wolf

Adam Burhan

Master's Research - Université de Montréal

Rodrigue De Schaetzen

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Glen Berseth

Anshul Gupta

Collaborating researcher - Université de Montréal

Co-supervisor :

Sarath Chandar

Website

Kumaraditya Gupta

Collaborating researcher

Co-supervisor :

Glen Berseth

Website

Zhen Liu

Collaborating Alumni - Université de Montréal

Co-supervisor :

Yoshua Bengio

Kaustubh Mani

PhD - Université de Montréal

Sacha Morin

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Yann Pequignot

Collaborating researcher - Université Laval

miguel-angel.saavedra-ruiz@mila.quebec

Luke Rowe

PhD - Université de Montréal

Co-supervisor :

Miguel Angel Saavedra Ruiz

PhD - Université de Montréal

Master's Research - Université de Montréal

How to Effectively and Efficiently Represent Non-Watertight Meshes for Your T-Shirts

Blog Posts

Visuel de l'Article sur la représentation du maillage non étanche de t-shirts

May 15, 2024

Zhen Liu

Yao Feng

Yuliang Xiu

Weiyang Liu

Liam Paull

Michael J. Black

Bernhard Scholkopf

Read the article

May 9, 2022

Sample Efficient Deep Reinforcement Learning Via Uncertainty Estimation

Vincent Mai

Kaustubh Mani

Liam Paull

Read the article

November 19, 2020

La-MAML: Look-ahead Meta-Learning for Continual Learning

Gunshi Gupta

Liam Paull

Read the article

Publications

Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience

Manfred Diaz

Andrea Tacchetti

Teacher-Student Curriculum Learning (TSCL) is a curriculum learning framework that draws inspiration from human cultural transmission and le… (see more)arning. It involves a teacher algorithm shaping the learning process of a learner algorithm by exposing it to controlled experiences. Despite its success, understanding the conditions under which TSCL is effective remains challenging. In this paper, we propose a data-centric perspective to analyze the underlying mechanics of the teacher-student interactions in TSCL. We leverage cooperative game theory to describe how the composition of the set of experiences presented by the teacher to the learner, as well as their order, influences the performance of the curriculum that is found by TSCL approaches. To do so, we demonstrate that for every TSCL problem, an equivalent cooperative game exists, and several key components of the TSCL framework can be reinterpreted using game-theoretic principles. Through experiments covering supervised learning, reinforcement learning, and classical games, we estimate the cooperative values of experiences and use value-proportional curriculum mechanisms to construct curricula, even in cases where TSCL struggles. The framework and experimental setup we present in this work represents a novel foundation for a deeper exploration of TSCL, shedding light on its underlying mechanisms and providing insights into its broader applicability in machine learning.

2024-04-03

ArXiv (preprint)

CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

Luke Rowe

Roger Girgis

Anthony Gosselin

Bruno Carrez

Florian Golemo

Felix Heide

Chris Pal

Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However… (see more), agents replayed from offline data are not reactive and hard to intuitively control. Existing approaches address these challenges by proposing methods that rely on heuristics or generative models of real-world data but these approaches either lack realism or necessitate costly iterative sampling procedures to control the generated behaviours. In this work, we take an alternative approach and propose CtRL-Sim, a method that leverages return-conditioned offline reinforcement learning (RL) to efficiently generate reactive and controllable traffic agents. Specifically, we process real-world driving data through a physics-enhanced Nocturne simulator to generate a diverse offline RL dataset, annotated with various rewards. With this dataset, we train a return-conditioned multi-agent behaviour model that allows for fine-grained manipulation of agent behaviours by modifying the desired returns for the various reward components. This capability enables the generation of a wide range of driving behaviours beyond the scope of the initial dataset, including adversarial behaviours. We show that CtRL-Sim can generate realistic safety-critical scenarios while providing fine-grained control over agent behaviours.

2024-03-29

ArXiv (preprint)

Correction to: Multi-agent reinforcement learning for fast-timescale demand response of residential loads

Vincent Mai

Philippe Maisonneuve

Tianyu Zhang

Hadi Nekoei

Antoine Lesage-Landry

2024-02-23

Machine-mediated learning (published)

Ghost on the Shell: An Expressive Representation of General 3D Shapes

Zhen Liu

Yao Feng

Yuliang Xiu

Weiyang Liu

Michael J. Black

Bernhard Schölkopf

2024-01-16

ICLR.cc/2024/Conference (oral)

GROOD: GRadient-aware Out-Of-Distribution detection in interpolated manifolds

Mostafa ElAraby

Sabyasachi Sahoo

Yann Batiste Pequignot

Paul Novello

2023-12-22

ArXiv (preprint)

Rethinking Teacher-Student Curriculum Learning under the Cooperative Mechanics of Experience

Manfred Diaz

Andrea Tacchetti

Teacher-Student Curriculum Learning (TSCL) is a curriculum learning framework that draws inspiration from human cultural transmission and le… (see more)arning. It involves a teacher algorithm shaping the learning process of a learner algorithm by exposing it to controlled experiences. Despite its success, understanding the conditions under which TSCL is effective remains challenging. In this paper, we propose a data-centric perspective to analyze the underlying mechanics of the teacher-student interactions in TSCL. We leverage cooperative game theory to describe how the composition of the set of experiences presented by the teacher to the learner, as well as their order, influences the performance of the curriculum that are found by TSCL approaches. To do so, we demonstrate that for every TSCL problem, there exists an equivalent cooperative game, and several key components of the TSCL framework can be reinterpreted using game-theoretic principles. Through experiments covering supervised learning, reinforcement learning, and classical games, we estimate the cooperative values of experiences and use value-proportional curriculum mechanisms to construct curricula, even in cases where TSCL struggles. The framework and experimental setup we present in this work represent a foundation that can be used for a deeper exploration of TSCL, shedding light on its underlying mechanisms and providing insights into its broader applicability in machine learning.

2023-10-28

NeurIPS.cc/2023/Workshop/ALOE (poster)

Ghost on the Shell: An Expressive Representation of General 3D Shapes

Zhen Liu

Yao Feng

Yuliang Xiu

Weiyang Liu

Michael J. Black

Bernhard Schölkopf

2023-10-23

ArXiv (preprint)

One-4-All: Neural Potential Fields for Embodied Navigation

Sacha Morin

Miguel Saavedra-Ruiz

A fundamental task in robotics is to navigate between two locations. In particular, real-world navigation can require long-horizon planning … (see more)using high-dimensional RGB images, which poses a substantial challenge for end-to-end learning-based approaches. Current semi-parametric methods instead achieve long-horizon navigation by combining learned modules with a topological memory of the environment, often represented as a graph over previously collected images. However, using these graphs in practice requires tuning a number of pruning heuristics. These heuristics are necessary to avoid spurious edges, limit runtime memory usage and maintain reasonably fast graph queries in large environments. In this work, we present One-4-All (O4A), a method leveraging self-supervised and manifold learning to obtain a graph-free, end-to-end navigation pipeline in which the goal is specified as an image. Navigation is achieved by greedily minimizing a potential function defined continuously over image embeddings. Our system is trained offline on non-expert exploration sequences of RGB data and controls, and does not require any depth or pose measurements. We show that 04A can reach long-range goals in 8 simulated Gibson indoor environments and that resulting embeddings are topologically similar to ground truth maps, even if no pose is observed. We further demonstrate successful real-world navigation using a Jackal UGV platform.aaProject page https://montrealrobotics.ca/o4a/.

2023-10-01

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)

Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss

Anas Mahmoud

Jordan S. K. Hu

Tianshu Kuai

Ali Harakeh

Steven L. Waslander

An effective framework for learning 3D representations for perception tasks is distilling rich self-supervised image features via contrastiv… (see more)e learning. However, image-to-point representation learning for autonomous driving datasets faces two main challenges: 1) the abundance of self-similarity, which results in the contrastive losses pushing away semantically similar point and image regions and thus disturbing the local semantic structure of the learned representations, and 2) severe class imbalance as pretraining gets dominated by over-represented classes. We propose to alleviate the self-similarity problem through a novel semantically tolerant image-to-point contrastive loss that takes into consideration the semantic distance between positive and negative image regions to minimize contrasting semantically similar point and image regions. Additionally, we address class imbalance by designing a class-agnostic balanced loss that approximates the degree of class imbalance through an aggregate sample-to-samples semantic similarity measure. We demonstrate that our semantically-tolerant contrastive loss with class balancing improves state-of-the-art 2D-to-3D representation learning in all evaluation settings on 3D semantic segmentation. Our method consistently outperforms state-of-the-art 2D-to-3D representation learning frameworks across a wide range of 2D self-supervised pretrained models.

2023-06-17

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

ConceptFusion: Open-set Multimodal 3D Mapping

Krishna Murthy

Alihusein Kuwajerwala

Qiao Gu

Mohd Omama

Tao Chen

Alaa Maalouf

Shuang Li

Ganesh Subramanian Iyer

Soroush Saryazdi

Nikhil Varma Keetha

Ayush Tewari

Joshua B. Tenenbaum

Celso M de Melo

Madhava Krishna

Florian Shkurti

Antonio Torralba

Building 3D maps of the environment is central to robot navigation, planning, and interaction with objects in a scene. Most existing approac… (see more)hes that integrate semantic concepts with 3D maps largely remain confined to the closed-set setting: they can only reason about a finite set of concepts, pre-defined at training time. Further, these maps can only be queried using class labels, or in recent work, using text prompts. We address both these issues with ConceptFusion, a scene representation that is: (i) fundamentally open-set, enabling reasoning beyond a closed set of concepts (ii) inherently multi-modal, enabling a diverse range of possible queries to the 3D map, from language, to images, to audio, to 3D geometry, all working in concert. ConceptFusion leverages the open-set capabilities of today’s foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio. We demonstrate that pixel-aligned open-set features can be fused into 3D maps via traditional SLAM and multi-view fusion approaches. This enables effective zero-shot spatial reasoning, not needing any additional training or finetuning, and retains long-tailed concepts better than supervised approaches, outperforming them by more than 40% margin on 3D IoU. We extensively evaluate ConceptFusion on a number of real-world datasets, simulated home environments, a real-world tabletop manipulation task, and an autonomous driving platform. We showcase new avenues for blending foundation models with 3D open-set multimodal mapping.

2023-05-06

ICRA.org/2023/Workshop/Pretraining4Robotics (published)

MeshDiffusion: Score-based Generative 3D Mesh Modeling

Zhen Liu

Yao Feng

Michael J. Black

Derek Nowrouzezahrai

Weiyang Liu

We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and… (see more) physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulation of shapes for relighting and simulation, and (2) they can fully leverage the power of modern graphics pipelines which are mostly optimized for meshes. Previous scalable methods for generating meshes typically rely on sub-optimal post-processing, and they tend to produce overly-smooth or noisy surfaces without fine-grained geometric details. To overcome these shortcomings, we take advantage of the graph structure of meshes and use a simple yet very effective generative modeling method to generate 3D meshes. Specifically, we represent meshes with deformable tetrahedral grids, and then train a diffusion model on this direct parameterization. We demonstrate the effectiveness of our model on multiple generative tasks.

2023-02-01

ICLR.cc/2023/Conference (notable)

Robust and Controllable Object-Centric Learning through Energy-based Models

Ruixiang ZHANG

Tong Che

Boris Ivanovic

Renhao Wang

Marco Pavone

Yoshua Bengio

Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability of decomposing low-level observations … (see more)into discrete objects allows us to build a grounded abstract representation and identify the compositional structure of the world. Thus it is a crucial step for machine learning models to be capable of inferring objects and their properties from visual scene without explicit supervision. However, existing works on object-centric representation learning are either relying on tailor-made neural network modules or assuming sophisticated models of underlying generative and inference processes. In this work, we present EGO, a conceptually simple and general approach to learning object-centric representation through energy-based model. By forming a permutation-invariant energy function using vanilla attention blocks that are readily available in Transformers, we can infer object-centric latent variables via gradient-based MCMC methods where permutation equivariance is automatically guaranteed. We show that EGO can be easily integrated into existing architectures, and can effectively extract high-quality object-centric representations, leading to better segmentation accuracy and competitive downstream task performance. We empirically evaluate the robustness of the learned representation from EGO against distribution shift. Finally, we demonstrate the effectiveness of EGO in systematic compositional generalization, by recomposing learned energy functions for novel scene generation and manipulation.

2023-02-01

ICLR.cc/2023/Conference (poster)