Portrait de Gregory Dudek n'est pas disponible

Gregory Dudek

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

Biographie

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

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
Superviseur⋅e principal⋅e :
David Meger
Github
Google Scholar
Steve Wen
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Doina Precup

Publications

View-Invariant Loop Closure with Oriented Semantic Landmarks
Jimmy Li
Karim Koreitem
David Meger
Gregory Dudek
Recent work on semantic simultaneous localization and mapping (SLAM) have shown the utility of natural objects as landmarks for improving lo… (voir plus)calization accuracy and robustness. In this paper we present a monocular semantic SLAM system that uses object identity and inter-object geometry for view-invariant loop detection and drift correction. Our system's ability to recognize an area of the scene even under large changes in viewing direction allows it to surpass the mapping accuracy of ORB-SLAM, which uses only local appearance-based features that are not robust to large viewpoint changes. Experiments on real indoor scenes show that our method achieves mean drift reduction of 70% when compared directly to ORB-SLAM. Additionally, we propose a method for object orientation estimation, where we leverage the tracked pose of a moving camera under the SLAM setting to overcome ambiguities caused by object symmetry. This allows our SLAM system to produce geometrically detailed semantic maps with object orientation, translation, and scale.
2019-12-31
IEEE International Conference on Robotics and Automation (publié)
doi.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.
2019-12-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Learning Domain Randomization Distributions for Transfer of Locomotion Policies
Melissa Mozian
Juan Higuera
David Meger
Gregory Dudek
Domain randomization (DR) is a successful technique for learning robust policies for robot systems, when the dynamics of the target robot sy… (voir plus)stem are unknown. The success of policies trained with domain randomization however, is highly dependent on the correct selection of the randomization distribution. The majority of success stories typically use real world data in order to carefully select the DR distribution, or incorporate real world trajectories to better estimate appropriate randomization distributions. In this paper, we consider the problem of finding good domain randomization parameters for simulation, without prior access to data from the target system. We explore the use of gradient-based search methods to learn a domain randomization with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution 2) The domain randomization distribution should be wide enough so that the experience similar to the target robot system is observed during training, while addressing the practicality of training finite capacity models. These two properties aim to ensure the trajectories encountered in the target system are close to those observed during training, as existing methods in machine learning are better suited for interpolation than extrapolation. We show how adapting the domain randomization distribution while training context-conditioned policies results in improvements on jump-start and asymptotic performance when transferring a learned policy to the target environment.
2019-06-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Semantic Mapping for View-Invariant Relocalization.
Jimmy Li
David Meger
Gregory Dudek
We propose a system for visual simultaneous localization and mapping (SLAM) that combines traditional local appearance-based features with s… (voir plus)emantically meaningful object landmarks to achieve both accurate local tracking and highly view-invariant object-driven relocalization. Our mapping process uses a sampling-based approach to efficiently infer the 3D pose of object landmarks from 2D bounding box object detections. These 3D landmarks then serve as a view-invariant representation which we leverage to achieve camera relocalization even when the viewing angle changes by more than 125 degrees. This level of view-invariance cannot be attained by local appearance-based features (e.g. SIFT) since the same set of surfaces are not even visible when the viewpoint changes significantly. Our experiments show that even when existing methods fail completely for viewpoint changes of more than 70 degrees, our method continues to achieve a relocalization rate of around 90%, with a mean rotational error of around 8 degrees.
2019-05-19
2019 International Conference on Robotics and Automation (ICRA) (publié)
doi.org
Planning in Dynamic Environments with Conditional Autoregressive Models
Johanna Hansen
Kyle Kastner
Aaron Courville
Gregory Dudek
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oo… (voir plus)rd et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obstacles. The combination of high-quality frame generation and classical planning approaches nearly matches true environment performance for our task, demonstrating the usefulness of this method for model-based planning in dynamic environments.
2018-11-24
ArXiv (prépublication)
arxiv.org
arxiv.org