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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

Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks
Dun Yuan
Yujin Nam
Amal Feriani
Abhisek Konar
Di Wu
Seowoo Jang
Xue (Steve) Liu
Gregory Dudek
Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G sys… (voir plus)tems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixed-variable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using a-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.
2023-06-01
ICC 2023 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Multi-Agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks
Jikun Kang
Di Wu
Ju Wang
Ekram Hossain
Xue (Steve) Liu
Gregory Dudek
In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the… (voir plus) BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-MA3C scheme can improve the overall performance by up to 45%.
2023-06-01
ICC 2023 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios
Yi Tian Xu
Jimmy Li
Di Wu
M. Jenkin
Seowoo Jang
Xue (Steve) Liu
Gregory Dudek
With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving incr… (voir plus)easing attention. Specifically, reinforcement learning (RL)-based methods have shown impressive performance compared with traditional rule-based methods. However, standard RL methods generally require an enormous amount of data to train, and generalize poorly to scenarios that are not encountered during training. We propose a policy reuse framework in which a policy selector chooses the most suitable pre-trained RL policy to execute based on the current traffic condition. Our method hinges on a policy bank composed of policies trained on a diverse set of traffic scenarios. When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training. Experiments demonstrate that this framework can outperform classical and adaptive rule-based methods by a large margin.
2023-06-01
ICC 2023 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Robust Scuba Diver Tracking and Recovery in Open Water Using YOLOv7, SORT, and Spiral Search
Faraz Lotfi
Khalil Virji
Gregory Dudek
Target tracking is a classic problem in computer vision, with numerous applications in robotics. However, tracking targets underwater presen… (voir plus)ts additional complications due to the six degrees of freedom nature of the problem and the challenging visual environment. In this paper, we address the problem of robotic underwater tracking of scuba divers by partitioning it into two parts: vision and control. We propose a new approach that exploits a highly-maneuverable underwater robot to perform experiments in open water, coupling sensing and control for improved performance. To evaluate the temporal stability of different tracking paradigms, we introduce a new metric, frame-to-frame vari-ance, which is better suited to assess the smoothness of detections from the vision side. We implement PID controllers for control and a spiral search algorithm for target recovery in case of a tracking failure. Our approach only uses observations in the image plane, eliminating the need for robot localization or camera calibration. Using a tracking-by-detection paradigm that combines YOLOv7 for target detection, a tuned filtering technique for temporal stability, and a spiral search algorithm for target recovery, we demonstrate promising performance for long-term tracking. We evaluate our proposed paradigm on the VDD-C dataset and deploy it on an underwater robot for several experiments in open water. Our outcomes show consistency with the ones in the initial studies, and the spiral search algorithm demonstrates promising performance for recapturing a target after a tracking failure. Our approach delivers promising performance for robust underwater tracking, achieving successful open-water tracking scenarios in the presence of strong water currents.
2023-06-01
Canadian Conference on Computer and Robot Vision (publié)
doi.org
Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks
Igor Kozlov
Dmitriy Rivkin
Wei-Di Chang
Di Wu
Xue (Steve) Liu
Gregory Dudek
Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thou… (voir plus)sands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance. The effects of such changes are challenging to predict and will become even more so with the adoption of fifth-generation/sixth-generation (5G/6G) networks. Therefore, RAN monitoring is vital for network operators. We propose a self-supervised learning framework that leverages self-attention and self-distillation for this task. It works by detecting changes in Performance Measurement data, a collection of time-varying metrics which reflect a set of diverse measurements of the network performance at the cell level. Experimental results show that our approach outperforms the state of the art by 4% on a real-world based dataset consisting of about hundred thousands time series. It also has the merits of being scalable and generalizable. This allows it to provide deep insight into the specifics of mode of operation changes while relying minimally on expert knowledge.
2023-06-01
ICC 2023 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Reinforcement learning for communication load balancing: approaches and challenges
Di Wu
Jimmy Li
Amal Ferini
Yi Tian Xu
M. Jenkin
Seowoo Jang
Xue (Steve) Liu
Gregory Dudek
The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for e… (voir plus)nhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.
2023-05-31
Frontiers of Computer Science (publié)
doi.org
Neural Bee Colony Optimization: A Case Study in Public Transit Network Design
Andrew Holliday
Gregory Dudek
In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization. We do this in t… (voir plus)he context of the transit network design problem, a uniquely challenging combinatorial optimization problem with real-world importance. We train a neural network policy to perform single-shot planning of individual transit routes, and then incorporate it as one of several sub-heuristics in a modified Bee Colony Optimization (BCO) metaheuristic algorithm. Our experimental results demonstrate that this hybrid algorithm outperforms the learned policy alone by up to 20% and the original BCO algorithm by up to 53% on realistic problem instances. We perform a set of ablations to study the impact of each component of the modified algorithm.
2023-05-18
ArXiv (prépublication)
doi.org
arxiv.org
Eliminating Space Scanning: Fast mmWave Beam Alignment with UWB Radios
Ju Wang
X. T. 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
IL-flOw: Imitation Learning from Observation using Normalizing Flows
Wei-Di Chang
Juan Higuera
Scott Fujimoto
David Meger
Gregory Dudek
2022-05-19
ArXiv (prépublication)
doi.org
arxiv.org
Learning Assisted Identification of Scenarios Where Network Optimization Algorithms Under-Perform
Dmitriy Rivkin
David Meger
Di Wu
X. T. Chen
Xue (Steve) Liu
Gregory Dudek
We present a generative adversarial method that uses deep learning to identify network load traffic conditions in which network optimization… (voir plus) algorithms under-perform other known algorithms: the Deep Convolutional Failure Generator (DCFG). The spatial distribution of network load presents challenges for network operators for tasks such as load balancing, in which a network optimizer attempts to maintain high quality communication while at the same time abiding capacity constraints. Testing a network optimizer for all possible load distributions is challenging if not impossible. We propose a novel method that searches for load situations where a target network optimization method underperforms baseline, which are key test cases that can be used for future refinement and performance optimization. By modeling a realistic network simulator's quality assessments with a deep network and, in parallel, optimizing a load generation network, our method efficiently searches the high dimensional space of load patterns and reliably finds cases in which a target network optimization method under-performs a baseline by a significant margin.
2021-12-01
Global Communications Conference (publié)
doi.org
Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain
Stefan Wapnick
Travis Manderson
David Meger
Gregory Dudek
We present a reward-predictive, model-based learning method featuring trajectory-constrained visual attention for use in mapless, local visu… (voir plus)al navigation tasks. Our method learns to place visual attention at locations in latent image space which follow trajectories caused by vehicle control actions to later enhance predictive accuracy during planning. Our attention model is jointly optimized by the task-specific loss and additional trajectory-constraint loss, allowing adaptability yet encouraging a regularized structure for improved generalization and reliability. Importantly, visual attention is applied in latent feature map space instead of raw image space to promote efficient planning. We validated our model in visual navigation tasks of planning low turbulence, collision-free trajectories in off-road settings and hill climbing with locking differentials in the presence of slippery terrain. Experiments involved randomized procedural generated simulation and real-world environments. We found our method improved generalization and learning efficiency when compared to no-attention and self-attention alternatives.
2021-10-01
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org
arxiv.org