21 citations · 38 across the 7 of their papers we have counts for
7 papers
From Prediction Uncertainty to Conformalized Distance Fields for Safe Motion Planning
Jaeuk Shin, Yoonseok Ra, Insoon Yang
Safe motion planning in dynamic environments requires reasoning about the uncertainty in predicted obstacle motion without sacrificing real-time performance. Existing conformal app…
KoopCast: Trajectory Forecasting via Koopman Operators
Jungjin Lee, Jaeuk Shin, Gihwan Kim +2
We present KoopCast, a lightweight yet efficient model for trajectory forecasting in general dynamic environments. Our approach leverages Koopman operator theory, which enables a l…
Egocentric Conformal Prediction for Safe and Efficient Navigation in Dynamic Cluttered Environments
Jaeuk Shin, Jungjin Lee, Insoon Yang
Conformal prediction (CP) has emerged as a powerful tool in robotics and control, thanks to its ability to calibrate complex, data-driven models with formal guarantees. However, in…
On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQR
Jaeuk Shin, Giho Kim, Howon Lee +2
Designing a competent meta-reinforcement learning (meta-RL) algorithm in terms of data usage remains a central challenge to be tackled for its successful real-world applications. I…
Anderson Acceleration for Partially Observable Markov Decision Processes: A Maximum Entropy Approach
Mingyu Park, Jaeuk Shin, Insoon Yang
Partially observable Markov decision processes (POMDPs) is a rich mathematical framework that embraces a large class of complex sequential decision-making problems under uncertaint…
Infusing model predictive control into meta-reinforcement learning for mobile robots in dynamic environments
Jaeuk Shin, Astghik Hakobyan, Mingyu Park +3
The successful operation of mobile robots requires them to adapt rapidly to environmental changes. To develop an adaptive decision-making tool for mobile robots, we propose a novel…
Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls
Jeongho Kim, Jaeuk Shin, Insoon Yang
In this paper, we propose Q-learning algorithms for continuous-time deterministic optimal control problems with Lipschitz continuous controls. Our method is based on a new class of…