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Gregory Dudek

Associate Academic Member
gregory.dudek@mila.quebec
Full Professor and Research Director of Mobile Robotics Lab, McGill University, School of Computer Science
Vice President and Lab Head of AI Research, Samsung AI Center in Montréal
Google Scholar

Biography

Gregory Dudek is a full professor at McGill University’s CIM which is linked to the School of Computer Science and Research Director of Mobile Robotics Lab. He is also the Lab Director and VP of research at Samsung AI Center Montreal and an Associate academic member at Mila - Quebec Institute of Artificial Intelligence.

Dudek has authored and co-authored over 300 research publications on a wide range of subjects, including visual object description, recognition, RF localization, robotic navigation and mapping, distributed system design, 5G telecommunications and biological perception.

He co-authored the book “Computational Principles of Mobile Robotics” (Cambridge University Press) with Michael Jenkin. He has chaired and been involved in numerous national and international conferences and professional activities concerned with robotics, machine sensing and computer vision.

Dudek’s research interests include perception for mobile robotics, navigation and position estimation, environment and shape modelling, computational vision and collaborative filtering.

Current Students

Farnoosh Faraji
PhD - McGill University
Principal supervisor :
David Meger
farnoosh.faraji@mila.quebec
Github
Google Scholar
Steve Wen
Master's Research - McGill University
Principal supervisor :
Doina Precup
steve.wen@mila.quebec

Publications

Learning to Adapt: Communication Load Balancing via Adaptive Deep Reinforcement Learning
Di Wu
Yi Tian Xu
Jimmy Li
M. Jenkin
Ekram Hossain
Seowoo Jang
Yan Xin
Charlie Zhang
Xue Liu
Gregory Dudek
The association of mobile devices with network resources (e.g., base stations, frequency bands/channels), known as load balancing, is critic… (see more)al to reduce communication traffic congestion and network performance. Reinforcement learning (RL) has shown to be effective for communication load balancing and achieves better performance than currently used rule-based methods, especially when the traffic load changes quickly. However, RL-based methods usually need to interact with the environment for a large number of time steps to learn an effective policy and can be difficult to tune. In this work, we aim to improve the data efficiency of RL-based solutions to make them more suitable and applicable for real-world applications. Specifically, we propose a simple, yet efficient and effective deep RL-based wireless network load balancing framework. In this solution, a set of good initialization values for control actions are selected with some cost-efficient approach to center the training of the RL agent. Then, a deep RL-based agent is trained to find offsets from the initialization values that optimize the load balancing problem. Experimental evaluation on a set of dynamic traffic scenarios demonstrates the effectiveness and efficiency of the proposed method.
2023-12-03
GLOBECOM 2023 - 2023 IEEE Global Communications Conference (published)
doi.org
A Study of Human-Robot Handover through Human-Human Object Transfer
Charlotte Morissette
Bobak H. Baghi
Francois Hogan
Gregory Dudek
In this preliminary study, we investigate changes in handover behaviour when transferring hazardous objects with the help of a high-resoluti… (see more)on touch sensor. Participants were asked to hand over a safe and hazardous object (a full cup and an empty cup) while instrumented with a modified STS sensor. Our data shows a clear distinction in the length of handover for the full cup vs the empty one, with the former being slower. Sensor data further suggests a change in tactile behaviour dependent on the object's risk factor. The results of this paper motivate a deeper study of tactile factors which could characterize a risky handover, allowing for safer human-robot interactions in the future.
2023-11-20
ArXiv (preprint)
doi.org
arxiv.org
SAGE: Smart home Agent with Grounded Execution
Dmitriy Rivkin
Francois Hogan
Amal Feriani
Abhisek Konar
Adam Sigal
Steve Liu
Gregory Dudek
2023-10-31
ArXiv (preprint)
doi.org
arxiv.org
Realizing XR Applications Using 5G-Based 3D Holographic Communication and Mobile Edge Computing
Dun Yuan
Ekram Hossain
Di Wu
Xue Liu
Gregory Dudek
3D holographic communication has the potential to revolutionize the way people interact with each other in virtual spaces, offering immersiv… (see more)e and realistic experiences. However, demands for high data rates, extremely low latency, and high computations to enable this technology pose a significant challenge. To address this challenge, we propose a novel job scheduling algorithm that leverages Mobile Edge Computing (MEC) servers in order to minimize the total latency in 3D holographic communication. One of the motivations for this work is to prevent the uncanny valley effect, which can occur when the latency hinders the seamless and real-time rendering of holographic content, leading to a less convincing and less engaging user experience. Our proposed algorithm dynamically allocates computation tasks to MEC servers, considering the network conditions, computational capabilities of the servers, and the requirements of the 3D holographic communication application. We conduct extensive experiments to evaluate the performance of our algorithm in terms of latency reduction, and the results demonstrate that our approach significantly outperforms other baseline methods. Furthermore, we present a practical scenario involving Augmented Reality (AR), which not only illustrates the applicability of our algorithm but also highlights the importance of minimizing latency in achieving high-quality holographic views. By efficiently distributing the computation workload among MEC servers and reducing the overall latency, our proposed algorithm enhances the user experience in 3D holographic communications and paves the way for the widespread adoption of this technology in various applications, such as telemedicine, remote collaboration, and entertainment.
2023-10-05
ArXiv (preprint)
doi.org
arxiv.org
A Generic Framework for Byzantine-Tolerant Consensus Achievement in Robot Swarms
Hanqing Zhao
Alexandre Pacheco
Volker Strobel
Andreagiovanni Reina
Xue Liu
Gregory Dudek
Marco Dorigo
Recent studies show that some security features that blockchains grant to decentralized networks on the internet can be ported to swarm robo… (see more)tics. Although the integration of blockchain technology and swarm robotics shows great promise, thus far, research has been limited to proof-of-concept scenarios where the blockchain-based mechanisms are tailored to a particular swarm task and operating environment. In this study, we propose a generic framework based on a blockchain smart contract that enables robot swarms to achieve secure consensus in an arbitrary observation space. This means that our framework can be customized to fit different swarm robotics missions, while providing methods to identify and neutralize Byzantine robots, that is, robots which exhibit detrimental behaviours stemming from faults or malicious tampering.
2023-09-30
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)
doi.org
Zero-Shot Fault Detection for Manipulators Through Bayesian Inverse Reinforcement Learning
Hanqing Zhao
Xue Liu
Gregory Dudek
We consider the detection of faults in robotic manipulators, with particular emphasis on faults that have not been observed or identified in… (see more) advance, which naturally includes those that occur very infrequently. Recent studies indicate that the reward function obtained through Inverse Reinforcement Learning (IRL) can help detect anomalies caused by faults in a control system (i.e. fault detection). Current IRL methods for fault detection, however, either use a linear reward representation or require extensive sampling from the environment to estimate the policy, rendering them inappropriate for safety-critical situations where sampling of failure observations via fault injection can be expensive and dangerous. To address this issue, this paper proposes a zero-shot and exogenous fault detector based on an approximate variational reward imitation learning (AVRIL) structure. The fault detector recovers a reward signal as a function of externally observable information to describe the normal operation, which can then be used to detect anomalies caused by faults. Our method incorporates expert knowledge through a customizable reward prior distribution, allowing the fault detector to learn the reward solely from normal operation samples, without the need for a simulator or costly interactions with the environment. We evaluate our approach for exogenous partial fault detection in multi-stage robotic manipulator tasks, comparing it with several baseline methods. The results demonstrate that our method more effectively identifies unseen faults even when they occur within just three controller time steps.
2023-09-30
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)
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… (see more)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.
2023-09-23
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) (published)
doi.org
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
Sahand Rezaei-Shoshtari
Charlotte Morissette
Francois R. Hogan
Gregory Dudek
David Meger
In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objec… (see more)tive and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.
2023-06-25
AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation
Amal Feriani
Di Wu
Steve Liu
Gregory Dudek
2023-06-22
ArXiv (preprint)
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… (see more)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.
2023-06-05
2023 20th Conference on Robots and Vision (CRV) (published)
doi.org
ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence
Dmitriy Rivkin
Gregory Dudek
Nikhil Kakodkar
David Meger
Oliver Limoyo
Xue Liu
Francois Hogan
Our work examines the way in which large language models can be used for robotic planning and sampling, specifically the context of automate… (see more)d photographic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.
2023-06-01
2023 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
arxiv.org
Communication Load Balancing via Efficient Inverse Reinforcement Learning
Abhisek Konar
Di Wu
Yi Tian Xu
Seowoo Jang
Steve Liu
Gregory Dudek
Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for net… (see more)work systems. After formulating the load balancing (LB) as a Markov decision process problem, reinforcement learning (RL) has recently proven effective in addressing the LB problem. To leverage the benefits of classical RL for load balancing, however, we need an explicit reward definition. Engineering this reward function is challenging, because it involves the need for expert knowledge and there lacks a general consensus on the form of an optimal reward function. In this work, we tackle the communication load balancing problem from an inverse reinforcement learning (IRL) approach. To the best of our knowledge, this is the first time IRL has been successfully applied in the field of communication load balancing. Specifically, first, we infer a reward function from a set of demonstrations, and then learn a reinforcement learning load balancing policy with the inferred reward function. Compared to classical RL-based solution, the proposed solution can be more general and more suitable for real-world scenarios. Experimental evaluations implemented on different simulated traffic scenarios have shown our method to be effective and better than other baselines by a considerable margin.
2023-05-31
ICC 2023 - IEEE International Conference on Communications (published)
doi.org
arxiv.org