111 citations · 200 across the 20 of their papers we have counts for
20 papers
Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip
Saki Omi, Hyo-Sang Shin, Namhoon Cho +2
Reinforcement learning (RL) performs well in continuous-control tasks, but evolving hidden disturbances create partial observability: the agent must infer decision-relevant latent…
A Passivity-Based Method for Accelerated Convex Optimisation
Namhoon Cho, Hyo-Sang Shin
This study presents a constructive methodology for designing accelerated convex optimisation algorithms in continuous-time domain. The two key enablers are the classical concept of…
Domain-Knowledge-Aided Airborne Ground Moving Targets Tracking
Jianduo Chai, Shaoming He, Hyo-Sang Shin
This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving targets tracking alg…
An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards
Ruifan Liu, Hyo-Sang Shin, Binbin Yan +1
In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution…
Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
Namhoon Cho, Hyo-Sang Shin, Antonios Tsourdos +1
This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved so…
Bayesian Learning Approach to Model Predictive Control
Namhoon Cho, Seokwon Lee, Hyo-Sang Shin +1
This study presents a Bayesian learning perspective towards model predictive control algorithms. High-level frameworks have been developed separately in the earlier studies on Baye…
Optimisation of Structured Neural Controller Based on Continuous-Time Policy Gradient
Namhoon Cho, Hyo-Sang Shin
This study presents a policy optimisation framework for structured nonlinear control of continuous-time (deterministic) dynamic systems. The proposed approach prescribes a structur…
Variational Probabilistic Multi-Hypothesis Tracking
Shuoyuan Xu, Hyo-Sang Shin, Antonios Tsourdos
This paper proposes a novel multi-target tracking (MTT) algorithm for scenarios with arbitrary numbers of measurements per target. We propose the variational probabilistic multi-hy…
A Learning-Based Computational Impact Time Guidance
Zichao Liu, Jiang Wang, Shaoming He +2
This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem. The proposed guidance algorithm is…
A New Exponential Forgetting Algorithm for Recursive Least-Squares Parameter Estimation
Hyo-Sang Shin, Hae-In Lee
This paper develops a new exponential forgetting algorithm that can prevent so-called the estimator windup problem, while retaining fast convergence speed. To investigate the prope…
Improving Learning Effectiveness For Object Detection and Classification in Cluttered Backgrounds
Vinorth Varatharasan, Hyo-Sang Shin, Antonios Tsourdos +1
Usually, Neural Networks models are trained with a large dataset of images in homogeneous backgrounds. The issue is that the performance of the network models trained could be sign…
Target Detection, Tracking and Avoidance System for Low-cost UAVs using AI-Based Approaches
Vinorth Varatharasan, Alice Shuang Shuang Rao, Eric Toutounji +2
An onboard target detection, tracking and avoidance system has been developed in this paper, for low-cost UAV flight controllers using AI-Based approaches. The aim of the proposed…
Threshold Greedy Based Task Allocation for Multiple Robot Operations
Teng Li, Hyo-Sang Shin, Antonios Tsourdos
This paper deals with large-scale decentralised task allocation problems for multiple heterogeneous robots with monotone submodular objective functions. One of the significant chal…
A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design
Hyo-Sang Shin, Shaoming He, Antonios Tsourdos
This paper aims to examine the potential of using the emerging deep reinforcement learning techniques in flight control. Instead of learning from scratch, we suggest to leverage do…
Sample Greedy Gossip for Distributed Network-Wide Average Computation
Hyo-Sang Shin, Shaoming He, Antonios Tsourdos
This paper investigates the problem of distributed network-wide averaging and proposes a new greedy gossip algorithm. Instead of finding the optimal path of each node in a greedy m…
Sample Greedy Based Task Allocation for Multiple Robot Systems
Hyo-Sang Shin, Teng Li, Pau Segui-Gasco
This paper addresses the task allocation problem for multi-robot systems. The main issue with the task allocation problem is inherent complexity that makes finding an optimal solut…
Fast submodular maximization subject to k-extendible system constraints
Teng Li, Hyo-Sang Shin, Antonios Tsourdos
As the scales of data sets expand rapidly in some application scenarios, increasing efforts have been made to develop fast submodular maximization algorithms. This paper presents a…
Bio-Inspired Local Information-Based Control for Probabilistic Swarm Distribution Guidance
Inmo Jang, Hyo-Sang Shin, Antonios Tsourdos
This paper addresses a task allocation problem for a large-scale robotic swarm, namely swarm distribution guidance problem. Unlike most of the existing frameworks handling this pro…
Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
Inmo Jang, Hyo-Sang Shin, Antonios Tsourdos
This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of se…
Fast Non-Monotone Submodular Maximisation Subject to a Matroid Constraint
Pau Segui-Gasco, Hyo-Sang Shin
In this work we present the first practical -approximation algorithm to maximise a general non-negative submodular function subject to a matroid constra…