Jisu Lee

I'm a candidate Ph.D at University of Hanyang in South Korea and studying Electrical Engineering.

I'm passionate about exploration and exploitation challenges in Reinforcement Learning (RL), Meta-RL, and Causal-RL. I'm currently researching how to optimize Meta-RL with Causal Representation Learning in mobile robot scenarios.

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Publications
C2A preview C2A detail
PRISM: Policy Regularization through Invariant Structural Modeling
Jisu Lee, Myoung Hoon Lee† and Jun Moon†
preprint.
project page
GOME-NGU preview GOME-NGU detail
GOME-NGU: visual navigation under sparse rewrd via Goal-Oriented Memory Encoder with Never Give Up
Jisu Lee, Jun Moon†
IEEE Access, 2025
project page / video

Miscellanea
Reinforcement Learning Basics with JAX (March. 2025 - TBD)
This project serves as a comprehensive guide for learning and implementing reinforcement learning (RL) algorithms using JAX. It is designed for both researchers and practitioners, providing structured tutorials and hands-on code implementations.
  • Covers fundamental RL topics such as Markov Decision Processes and Q-learning.
  • Explores advanced methods, including policy gradient techniques and model-based RL.
Paper Reviews (April. 2025 - TBD)
Paper Reviews is a curated platform offering in-depth reviews of cutting-edge research in AI, Machine Learning, and Robotics. Designed for both researchers and practitioners, it provides comprehensive insights, accessible explanations, and regular updates on influential publications.
  • Curated collection of influential research papers across diverse AI domains.
  • Detailed reviews and technical analyses that simplify complex concepts.

Projects
NVIDIA Isaac-Sim Mobile Robot Simulator (May. 2024 - TBD)
We are proceeding with the NVIDIA Isaac-Sim Mobile Robot (Franka Research 3, Husky, QCar, Husky + Franka Research 3) unified platform to ease usage for beginner to master courses.
We are developing a new user interface (UI) in the NVIDIA Isaac-Sim.
CARLA Semantic Segmentation Challenge (May. 2023 - May. 2023)
Creating a semantic segmentation model for autonomous driving in the CARLA environment using a pre-trained model.
Achieved real-time segmentation under diverse weather conditions.
HyperDrive: Advanced Reinforcement Learning for Autonomous Racing (TBD)
A research-driven project exploring advanced reinforcement learning techniques for autonomous racing, conducted using the Learn to Race simulation platform.
Developed and evaluated various reinforcement learning algorithms, including Online RL, Offline RL, Transformer-based RL, State Space Models, and Meta-RL.
Drone Position Control using Reinforcement Learning (Aug. 2025 - Sep. 2025)
A collaborative research project with KAUST laboratory focusing on autonomous drone control using reinforcement learning techniques for precise position control.
Developed and implemented RL-based position control algorithms for quadrotor drones in complex 3D environments.

Updated April 2026.

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