Portrait de David Meger

David Meger

Membre académique associé
Professeur adjoint, McGill University, École d'informatique
Sujets de recherche
Apprentissage par renforcement
Vision par ordinateur

Biographie

David Meger est professeur adjoint à l'École d'informatique de l'Université McGill. Il codirige le Laboratoire de robotique mobile au sein du Centre sur les machines intelligentes, qui est l'un des groupes de recherche en robotique les plus importants et les plus anciens du Canada. Les travaux de recherche du professeur Meger portent notamment sur les robots à guidage visuel dotés d'une vision et d'un apprentissage actifs, sur les modèles d'apprentissage par renforcement profond qui sont largement cités et utilisés par les chercheurs et l'industrie dans le monde entier, et sur la robotique de terrain, y compris les déploiements autonomes sous l'eau et sur la terre ferme. Il a été le président général de la première conférence conjointe CS-CAN au Canada en 2023.

Étudiants actuels

Julie Alhosh
Maîtrise recherche - McGill
Site web
Github
Google Scholar
Rambod Azimi
Collaborateur·rice de recherche - McGill
Superviseur⋅e principal⋅e :
Samira Ebrahimi Kahou
Site web
Github
Google Scholar
William Bonilla
Doctorat - McGill
Github
Valliappan Chidambaram Adaikkappan
Doctorat - McGill
Github
Google Scholar
Wesley Chung
Doctorat - McGill
Co-superviseur⋅e :
Doina Precup
Farnoosh Faraji
Doctorat - McGill
Co-superviseur⋅e :
Gregory Dudek
Github
Google Scholar
Anas Houssaini
Maîtrise recherche - McGill
Co-superviseur⋅e :
Hsiu-Chin Lin
Github
Zina Kamel
Maîtrise recherche - McGill
Co-superviseur⋅e :
Hsiu-Chin Lin
Sahand Rezaei-Shoshtari
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doina Precup
Site web
Github
Google Scholar
Arian Sargazi
Doctorat - McGill
Steven Wang
Maîtrise recherche - McGill
Harley Wiltzer
Doctorat - McGill
Co-superviseur⋅e :
Marc Gendron-Bellemare
Site web
Github
Google Scholar
Hanna Yurchyk
Doctorat - McGill

Publications

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Scott Fujimoto
David Meger
Doina Precup
Prioritized Experience Replay (PER) is a deep reinforcement learning technique in which agents learn from transitions sampled with non-unifo… (voir plus)rm probability proportionate to their temporal-difference error. We show that any loss function evaluated with non-uniformly sampled data can be transformed into another uniformly sampled loss function with the same expected gradient. Surprisingly, we find in some environments PER can be replaced entirely by this new loss function without impact to empirical performance. Furthermore, this relationship suggests a new branch of improvements to PER by correcting its uniformly sampled loss function equivalent. We demonstrate the effectiveness of our proposed modifications to PER and the equivalent loss function in several MuJoCo and Atari environments.
arxiv.org
Seeing Through Your Skin: A Novel Visuo-Tactile Sensor for Robotic Manipulation
Francois Hogan
Sahand Rezaei-Shoshtari
M. Jenkin
Yashveer Girdhar
David Meger
Gregory Dudek
This work describes the development of the novel tactile sensor, named Semitransparent Tactile Sensor (STS), designed to enable reactive and… (voir plus) robust manipulation skills. The design, inspired from recent developments in optical tactile sensing technology, addresses a key missing features of these sensors: the ability to capture an “in the hand” perspective prior to and during the contact interaction. Whereas optical tactile sensors are typically opaque and obscure the view of the object at the critical moment prior to manipulator-object contact, we present a sensor that has the dual capabilities of acting as a tactile sensor and as a visual camera. This paper details the design and fabrication of the sensor, showcases its dual sensing capabilities, and introduces a simulated environment of the sensor within the PyBullet simulator.
3D Shape Reconstruction from Vision and Touch
Edward J. Smith
Roberto Calandra
Adriana Romero Soriano
Georgia Gkioxari
David Meger
Jitendra Malik
Michal Drozdzal
When a toddler is presented a new toy, their instinctual behaviour is to pick it up and inspect it with their hand and eyes in tandem, clear… (voir plus)ly searching over its surface to properly understand what they are playing with. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) the reconstruction quality increases with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.
arxiv.org
Detecting GAN generated errors
Xiru Zhu
Fengdi Che
Tianzi Yang
Tzuyang Yu
David Meger
Gregory Dudek
Despite an impressive performance from the latest GAN for generating hyper-realistic images, GAN discriminators have difficulty evaluating t… (voir plus)he quality of an individual generated sample. This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake. A generated image could be perfect except in a single area but still be detected as fake. Instead, we propose a novel approach for detecting where errors occur within a generated image. By collaging real images with generated images, we compute for each pixel, whether it belongs to the real distribution or generated distribution. Furthermore, we leverage attention to model long-range dependency; this allows detection of errors which are reasonable locally but not holistically. For evaluation, we show that our error detection can act as a quality metric for an individual image, unlike FID and IS. We leverage Improved Wasserstein, BigGAN, and StyleGAN to show a ranking based on our metric correlates impressively with FID scores. Our work opens the door for better understanding of GAN and the ability to select the best samples from a GAN model.
2019-12-02
ArXiv (prépublication)
arxiv.org
arxiv.org