Jaeuk Shin — accepted AI-conference papers

2026

KoopCast: Trajectory Forecasting Via Koopman Operators

Jungjin Lee, Jaeuk Shin, Gihwan Kim, Joon Ho Han, Insoon Yang

ICRA 2026poster

We present KoopCast, a lightweight yet efficient model for trajectory forecasting in general dynamic environments. Our approach leverages Koopman operator theory, which enables a linear representation of nonlinear dynamics by lifting trajectories into a higher-dimensional space. The framework follow…

Cited by 0 Source ↗PDF ↗Scholar ↗

2022

Infusing Model Predictive Control Into Meta-Reinforcement Learning for Mobile Robots in Dynamic Environments

Jaeuk Shin, Astghik Hakobyan, Mingyu Park, Yeoneung Kim, Gihun Kim, Insoon Yang

RA-L 2022

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 algorithm that combines meta-reinforcement learning (meta-RL) with model predictive control (MPC). Our method employs a

Cited by 15 Source ↗code ↗Scholar ↗