4 citations · 7 across the 22 of their papers we have counts for
23 papers
Active Noise Floor Estimation for Reliability-Optimal POMDPs: A Value-of-Noise-Information Approach
Hyung-Jin Yoon
Finite Reliability Representations (FRR) certify when a cell-constant policy is sufficient for reliable decision-making in a partially observed system with a known physical noise f…
Adaptive MPPI with Online Disturbance Covariance Estimation: Provable Stability Tightening via Spatial Smoothing
Hyung-Jin Yoon, Hunmin Kim
We study Model Predictive Path Integral (MPPI) control for nonlinear systems with additive process disturbances whose covariance is unknown, spatially varying, and slowly time-vary…
Residual-Conservative Model Predictive Path Integral Control
Hyung-Jin Yoon, Hunmin Kim
Sampling-based model predictive control methods handle nonlinear dynamics and complex cost landscapes through Monte Carlo rollouts, yet typically employ fixed constraint penalties…
Stochastic Stability of Nonlinear MPPI via Contraction Theory and Control Lyapunov Functions
Hyung-Jin Yoon, Hunmin Kim
Model Predictive Path Integral (MPPI) control is directly implementable on nonlinear systems because its online update requires only forward rollouts of the dynamics, not gradients…
Finite Reliability Representations: Noise-Calibrated Belief-Space Covers for Reliable Decision-Making
Hyung-Jin Yoon, Hunmin Kim
Physical sensing and actuation noise floors should inform how much belief resolution a decision-making system can reliably use. We introduce Finite Reliability Representations (FRR…
Finite-Sample Closed-Loop Stability of Model Predictive Path Integral Control for Linear Time-Invariant Systems
Hyung-Jin Yoon, Hunmin Kim
We establish finite-sample closed-loop stability guarantees for Model Predictive Path Integral (MPPI) control applied to discrete-time Linear Time-Invariant (LTI) systems with addi…
Synergistic Perception and Control Simplex for Verifiable Safe Vertical Landing
Ayoosh Bansal, Yang Zhao, James Zhu +6
Perception, Planning, and Control form the essential components of autonomy in advanced air mobility. This work advances the holistic integration of these components to enhance the…
Backup Plan Constrained Model Predictive Control with Guaranteed Stability
Ran Tao, Hunmin Kim, Hyung-Jin Yoon +4
This article proposes and evaluates a new safety concept called backup plan safety for path planning of autonomous vehicles under mission uncertainty using model predictive control…
Learning When to Use Adaptive Adversarial Image Perturbations against Autonomous Vehicles
Hyung-Jin Yoon, Hamidreza Jafarnejadsani, Petros Voulgaris
The deep neural network (DNN) models for object detection using camera images are widely adopted in autonomous vehicles. However, DNN models are shown to be susceptible to adversar…
Multi-time Predictions of Wildfire Grid Map using Remote Sensing Local Data
Hyung-Jin Yoon, Petros Voulgaris
Due to recent climate changes, we have seen more frequent and severe wildfires in the United States. Predicting wildfires is critical for natural disaster prevention and mitigation…
Path Integral Methods with Stochastic Control Barrier Functions
Chuyuan Tao, Hyung-Jin Yoon, Hunmin Kim +2
Safe control designs for robotic systems remain challenging because of the difficulties of explicitly solving optimal control with nonlinear dynamics perturbed by stochastic noise.…
Sampling Complexity of Path Integral Methods for Trajectory Optimization
Hyung-Jin Yoon, Chuyuan Tao, Hunmin Kim +2
The use of random sampling in decision-making and control has become popular with the ease of access to graphic processing units that can generate and calculate multiple random tra…
Learning Wildfire Model from Incomplete State Observations
Alissa Chavalithumrong, Hyung-Jin Yoon, Petros Voulgaris
As wildfires are expected to become more frequent and severe, improved prediction models are vital to mitigating risk and allocating resources. With remote sensing data, valuable s…
Control Barrier Function Augmentation in Sampling-based Control Algorithm for Sample Efficiency
Chuyuan Tao, Hunmin Kim, Hyungjin Yoon +2
For a nonlinear stochastic path planning problem, sampling-based algorithms generate thousands of random sample trajectories to find the optimal path while guaranteeing safety by L…
Time Coordination of Multiple UAVs over Switching Communication Networks with Digraph Topologies
Hyungsoo Kang, Hyung-Jin Yoon, Venanzio Cichella +2
This paper presents a time-coordination algorithm for multiple UAVs executing cooperative missions. Unlike previous algorithms, it does not rely on the assumption that the communic…
Learning Image Attacks toward Vision Guided Autonomous Vehicles
Hyung-Jin Yoon, Hamidreza Jafarnejadsani, Petros Voulgaris
While adversarial neural networks have been shown successful for static image attacks, very few approaches have been developed for attacking online image streams while taking into…
Backup Plan Constrained Model Predictive Control
Hunmin Kim, Hyungjin Yoon, Wenbin Wan +3
This article proposes a new safety concept: backup plan safety. The backup plan safety is defined as the ability to complete one of the alternative missions in the case of primary…
Estimation and Planning of Exploration Over Grid Map Using A Spatiotemporal Model with Incomplete State Observations
Hyung-Jin Yoon, Hunmin Kim, Kripash Shrestha +2
Path planning over spatiotemporal models can be applied to a variety of applications such as UAVs searching for spreading wildfire in mountains or network of balloons in time-varyi…
Towards Resilient UAV: Escape Time in GPS Denied Environment with Sensor Drift
Hyung-Jin Yoon, Wenbin Wan, Hunmin Kim +3
This paper considers a resilient state estimation framework for unmanned aerial vehicles (UAVs) that integrates a Kalman filter-like state estimator and an attack detector. When an…
A Path Planning Framework for a Flying Robot in Close Proximity of Humans
Hyung-Jin Yoon, Christopher Widdowson, Thiago Marinho +2
We present a path planning framework that takes into account the human's safety perception in the presence of a flying robot. The framework addresses two objectives: (i) estimation…
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
Hyung-Jin Yoon, Huaiyu Chen, Kehan Long +4
We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observat…
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process
Hyung-Jin Yoon, Donghwan Lee, Naira Hovakimyan
The objective is to study an on-line Hidden Markov model (HMM) estimation-based Q-learning algorithm for partially observable Markov decision process (POMDP) on finite state and ac…
Primal-Dual Algorithm for Distributed Reinforcement Learning: Distributed GTD
Donghwan Lee, Hyungjin Yoon, Naira Hovakimyan
The goal of this paper is to study a distributed version of the gradient temporal-difference (GTD) learning algorithm for multi-agent Markov decision processes (MDPs). The temporal…