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Gregory Dudek

Membre académique associé
Professeur titulaire, McGill University, École d'informatique
Vice-président et Chef de laboratoire de la recherche du Centre d'intelligence artificielle, Samsung AI Center in Montréal

Biographie

Gregory Dudek est professeur au Centre sur les machines intelligentes (CIM) de l’École d’informatique de l’Université McGill et directeur de recherche du Laboratoire de robotique mobile de l’université. Il est également chef de laboratoire et vice-président de la recherche du Centre d’intelligence artificielle de Samsung à Montréal.

Il a écrit, seul ou en collaboration, plus de 300 articles de recherche sur des sujets tels que la description et la reconnaissance d’objets visuels, la localisation de radiofréquences (RF), la navigation et la cartographie robotiques, la conception de systèmes distribués, les télécommunications 5G et la perception biologique. Il a notamment publié le livre Computational Principles of Mobile Robotics, en collaboration avec Michael Jenkin, aux éditions Cambridge University Press. Il a présidé ou a contribué à de nombreuses conférences et activités professionnelles nationales et internationales dans les domaines de la robotique, de la détection par machine et de la vision par ordinateur. Ses recherches portent sur la perception pour la robotique mobile, la navigation et l’estimation de la position, la modélisation de l’environnement et des formes, la vision informatique et le filtrage collaboratif.

Étudiants actuels

Farnoosh Faraji
Doctorat - McGill University
Superviseur⋅e principal⋅e :
David Meger
farnoosh.faraji@mila.quebec
Github
Google Scholar
Gaspard Zhao
Postdoctorat - McGill University
Superviseur⋅e principal⋅e :
Xue (Steve) Liu
hanqing.zhao@mila.quebec
Site web

Publications

Eliminating Space Scanning: Fast mmWave Beam Alignment with UWB Radios
Ju Wang
Xi Chen
Xue (Steve) Liu
Gregory Dudek
Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond… (voir plus) (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays. To eliminate the need for complex space scanning, this article presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge of applying this idea in the real world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a small number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUltiple SIgnal Classification (MF-MUSIC) algorithm, which extends the classic MUltiple SIgnal Classification (MUSIC) algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. Extensive real-world experiments and numerical simulations illustrate the advantage of the proposed MF-MUSIC algorithm. MF-MUSIC uses only three antennas to achieve an accurate angle estimation, which is a mere 0.15° (or a relative difference of 3.6%) different from the state-of-the-art 16-antenna-based angle estimation method.
2023-05-16
ACM Transactions on Sensor Networks (publié)
doi.org
Augmenting Transit Network Design Algorithms with Deep Learning
Andrew Holliday
Gregory Dudek
This paper considers the use of deep learning models to enhance optimization algorithms for transit network design. Transit network design i… (voir plus)s the problem of determining routes for transit vehicles that minimize travel time and operating costs, while achieving full service coverage. State-of-the-art meta-heuristic search algorithms give good results on this problem, but can be very time-consuming. In contrast, neural networks can learn sub-optimal but fast-to-compute heuristics based on large amounts of data. Combining these approaches, we develop a fast graph neural network model for transit planning, and use it to initialize state-of-the-art search algorithms. We show that this combination can improve the results of these algorithms on a variety of metrics by up to 17%, without increasing their run time; or they can match the quality of the original algorithms while reducing the computing time by up to a factor of 50.
2023-01-01
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) (publié)
doi.org
Learning active tactile perception through belief-space control
Jean-François Tremblay
Johanna Hansen
David Meger
Francois Hogan
Gregory Dudek
Robot operating in an open world can encounter novel objects with unknown physical properties, such as mass, friction, or size. It is desira… (voir plus)ble to be able to sense those property through contact-rich interaction, before performing downstream tasks with the objects. We propose a method for autonomously learning active tactile perception policies, by learning a generative world model leveraging a differentiable bayesian filtering algorithm, and designing an information- gathering model predictive controller. We test the method on three simulated tasks: mass estimation, height estimation and toppling height estimation. Our method is able to discover policies which gather information about the desired property in an intuitive manner.
2022-05-12
ICRA.org/2022/Workshop/Contact-Rich (poster)
doi.org
openreview.net