Publications

Generating Stable and Collision-Free Policies through Lyapunov Function Learning
Alexandre Coulombe
Hsiu-Chin Lin
The need for rapid and reliable robot deployment is on the rise. Imitation Learning (IL) has become popular for producing motion planning po… (voir plus)licies from a set of demonstrations. However, many methods in IL are not guaranteed to produce stable policies. The generated policy may not converge to the robot target, reducing reliability, and may collide with its environment, reducing the safety of the system. Stable Estimator of Dynamic Systems (SEDS) produces stable policies by constraining the Lyapunov stability criteria during learning, but the Lyapunov candidate function had to be manually selected. In this work, we propose a novel method for learning a Lyapunov function and a collision-free policy using a single neural network model. The method can be equipped with an obstacle avoidance module for convex object pairs to guarantee no collisions. We demonstrated our method is capable of finding policies in several simulation environments and transfer to a real-world scenario.
2023-06-02
2023 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans
Stefania Raimondo
Chris Pal
Xiaotian Liu
David Vazquez
Hector. Palacios
2023-06-02
ArXiv (prépublication)
doi.org
arxiv.org
Predicting Time to and Average Quality of Future Offers for Kidney Transplant Candidates Declining a Current Deceased Donor Kidney Offer: A Retrospective Cohort Study
Jonathan Jalbert
Jean-Noel Weller
Pierre-Luc Boivin
Sylvain Lavigne
Mehdi Taobane
Mike Pieper
Andrea Lodi
Heloise Cardinal
2023-06-02
Canadian Journal of Kidney Health and Disease (publié)
doi.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… (voir plus)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.
2023-06-01
ICC 2023 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Discussion of “Experimental Study of the Thixotropic Strength Recovery and Microstructural Evolution of Marine Clays”
Xianwei Zhang
Xinyu Liu
Gang Wang
2023-06-01
Journal of Geotechnical and Geoenvironmental Engineering (published)
doi.org
Discussion of “Experimental Study of the Thixotropic Strength Recovery and Microstructural Evolution of Marine Clays”
Xianwei Zhang
Xinyu Liu
Gang Wang
2023-06-01
Journal of Geotechnical and Geoenvironmental Engineering (publié)
doi.org
Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data.
Guillaume Lam
Irina Rish
P. Dixon
2023-06-01
Journal of Biomechanics (publié)
doi.org
Fast Fine-Tuning Using Curriculum Domain Adaptation
Lulan Shen
Ibtihel Amara
Ruofeng Li
Brett Meyer
Warren Gross
James J. Clark
Current deep neural networks (DNNs) have achieved remarkable accuracy in various downstream tasks. However, their training and fine-tuning a… (voir plus)re challenging due to several factors, such as limited computational resources, extended training and fine-tuning times, and over-fitting due to small datasets. To address these challenges, we propose a three-stage fast fine-tuning method that efficiently trains DNNs for edge devices. Our method combines curriculum learning and domain adaptation techniques to accelerate training while achieving comparable performance. First, we develop a data curriculum approach, which ranks the dataset according to difficulty and split it into the source domain (containing easy data) and the target domain (containing difficult data). Second, we adapt the pretrained model from the source domain to the target domain using an unsupervised domain adaptation (UDA) method called Deep CORAL. Finally, we continue training the adapted model on the source domain with fewer epochs. Our method achieves high accuracy quickly on various modern neural network architectures and datasets such as CIFAR-10, CIFAR-100, and CINIC-10.
2023-06-01
Canadian Conference on Computer and Robot Vision (publié)
doi.org
Geometry Regularized Autoencoders
Andres F. Duque Correa
Sacha Morin
Guy Wolf
Kevin R. Moon
A fundamental task in data exploration is to extract low dimensional representations that capture intrinsic geometry in data, especially for… (voir plus) faithfully visualizing data in two or three dimensions. Common approaches use kernel methods for manifold learning. However, these methods typically only provide an embedding of the input data and cannot extend naturally to new data points. Autoencoders have also become popular for representation learning. While they naturally compute feature extractors that are extendable to new data and invertible (i.e., reconstructing original features from latent representation), they often fail at representing the intrinsic data geometry compared to kernel-based manifold learning. We present a new method for integrating both approaches by incorporating a geometric regularization term in the bottleneck of the autoencoder. This regularization encourages the learned latent representation to follow the intrinsic data geometry, similar to manifold learning algorithms, while still enabling faithful extension to new data and preserving invertibility. We compare our approach to autoencoder models for manifold learning to provide qualitative and quantitative evidence of our advantages in preserving intrinsic structure, out of sample extension, and reconstruction. Our method is easily implemented for big-data applications, whereas other methods are limited in this regard.
2023-06-01
IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)
doi.org
Grow-push-prune: Aligning deep discriminants for effective structural network compression
Qing Tian
Tal Arbel
James J. Clark
2023-06-01
Computer Vision and Image Understanding (publié)
doi.org
arxiv.org
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