3 citations · 9 across the 20 of their papers we have counts for
23 papers
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization
Jimmy Li, Igor Kozlov, Di Wu +2
The use of learning-based methods for optimizing cellular radio access networks (RAN) has received increasing attention in recent years. This coincides with a rapid increase in the…
Learning active tactile perception through belief-space control
Jean-François Tremblay, David Meger, Francois Hogan +1
Robots operating in an open world will encounter novel objects with unknown physical properties, such as mass, friction, or size. These robots will need to sense these properties t…
PEOPLEx: PEdestrian Opportunistic Positioning LEveraging IMU, UWB, BLE and WiFi
Pierre-Yves Lajoie, Bobak Hamed Baghi, Sachini Herath +3
This paper advances the field of pedestrian localization by introducing a unifying framework for opportunistic positioning based on nonlinear factor graph optimization. While many…
Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching
Junliang Luo, Yi Tian Xu, Di Wu +3
Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental and regulatory concerns, and potential en…
Realizing XR Applications Using 5G-Based 3D Holographic Communication and Mobile Edge Computing
Dun Yuan, Ekram Hossain, Di Wu +2
3D holographic communication has the potential to revolutionize the way people interact with each other in virtual spaces, offering immersive and realistic experiences. However, de…
CARTIER: Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots
Dmitriy Rivkin, Nikhil Kakodkar, Francois Hogan +2
This work explores the capacity of large language models (LLMs) to address problems at the intersection of spatial planning and natural language interfaces for navigation. We focus…
CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation
Amal Feriani, Di Wu, Steve Liu +1
Deep learning has been extensively used in wireless communication problems, including channel estimation. Although several data-driven approaches exist, a fair and realistic compar…
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 the context of the transit network design…
Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks
Dun Yuan, Yujin Nam, Amal Feriani +5
Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G systems enabling the design of machine lear…
Communication Load Balancing via Efficient Inverse Reinforcement Learning
Abhisek Konar, Di Wu, Yi Tian Xu +3
Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for network systems. After formulating the load…
Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios
Yi Tian Xu, Jimmy Li, Di Wu +4
With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving increasing attention. Specifically, reinforc…
Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks
Jikun Kang, Di Wu, Ju Wang +3
In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs. To address this problem, BSs can w…
ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence
Dmitriy Rivkin, Gregory Dudek, Nikhil Kakodkar +4
Our work examines the way in which large language models can be used for robotic planning and sampling, specifically the context of automated photographic documentation. Specifical…
Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks
Igor Kozlov, Dmitriy Rivkin, Wei-Di Chang +3
Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thousands of transmitting devices (cells). S…
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois Robert Hogan +2
In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal…
Bayesian Q-learning With Imperfect Expert Demonstrations
Fengdi Che, Xiru Zhu, Doina Precup +2
Guided exploration with expert demonstrations improves data efficiency for reinforcement learning, but current algorithms often overuse expert information. We propose a novel algor…
IL-flOw: Imitation Learning from Observation using Normalizing Flows
Wei-Di Chang, Juan Camilo Gamboa Higuera, Scott Fujimoto +2
We present an algorithm for Inverse Reinforcement Learning (IRL) from expert state observations only. Our approach decouples reward modelling from policy learning, unlike state-of-…
Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain
Stefan Wapnick, Travis Manderson, David Meger +1
We present a reward-predictive, model-based deep learning method featuring trajectory-constrained visual attention for local planning in visual navigation tasks. Our method learns…
Sample Efficient Social Navigation Using Inverse Reinforcement Learning
Bobak H. Baghi, Gregory Dudek
In this paper, we present an algorithm to efficiently learn socially-compliant navigation policies from observations of human trajectories. As mobile robots come to inhabit and tra…
Learning Intuitive Physics with Multimodal Generative Models
Sahand Rezaei-Shoshtari, Francois Robert Hogan, Michael Jenkin +2
Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelligent and anticipatory actions. This pape…
Multimodal dynamics modeling for off-road autonomous vehicles
Jean-François Tremblay, Travis Manderson, Aurélio Noca +2
Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robot in ways that can be hard to predict. L…
Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles
Travis Manderson, Juan Camilo Gamboa Higuera, Stefan Wapnick +4
We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train a navigation policy that enables a robot…
One-Shot Informed Robotic Visual Search in the Wild
Karim Koreitem, Florian Shkurti, Travis Manderson +3
We consider the task of underwater robot navigation for the purpose of collecting scientifically relevant video data for environmental monitoring. The majority of field robots that…