23 citations · 57 across the 7 of their papers we have counts for
7 papers
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning
Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae +2
We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy…
Informative Planning of Mobile Sensor Networks in GPS-Denied Environments
Youngjae Min, Soon-Seo Park, Han-Lim Choi
This paper considers the problem to plan mobile sensor networks for target localization task in GPS-denied environments. Most researches on mobile sensor networks assume that the s…
Online Gaussian Process State-Space Model: Learning and Planning for Partially Observable Dynamical Systems
Soon-Seo Park, Young-Jin Park, Youngjae Min +1
This paper proposes an online learning method of Gaussian process state-space model (GP-SSM). GP-SSM is a probabilistic representation learning scheme that represents unknown state…
Potential Game-Based Non-Myopic Sensor Network Planning for Multi-Target Tracking
Su-Jin Lee, Soon-Seo Park, Han-Lim Choi
This paper presents a potential game-based method for non-myopic planning of mobile sensor networks in the context of target tracking. The planning objective is to select the seque…
A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking
Soon-Seo Park, Youngjae Min, Jung-Su Ha +2
This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of h…
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae +2
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional \textit{sequential} raw data, e.g., video. The framework…
Topology-Guided Path Integral Approach for Stochastic Optimal Control in Cluttered Environment
Jung-Su Ha, Soon-Seo Park, Han-Lim Choi
This paper addresses planning and control of robot motion under uncertainty that is formulated as a continuous-time, continuous-space stochastic optimal control problem, by develop…