Jeonghwan Kim

  I'm a Robotics PhD student at Georgia Tech, advised by Dr. Sehoon Ha. I received my Master's degrees in ECE and Math from Georgia Tech, and my Bachelor's degree from Seoul National University, majoring in Electrical and Computer Engineering.
  I love working on robot learning, computer animation, and ML-based control. My goal is to develop algorithms that enable robots to seamlessly interact in everyday environments.



Experience
Georgia Tech
PhD in Robotics
Georgia Tech
2023 - Present
RAI Institute
Research Intern
RAI Institute
2024.09 - 2025.04


Publications
Flip Stunts on Bike-Like Robots using Iterative Motion Imitation
Jeonghwan Kim, Shamel Fahmi, Seungeun Rho, Sehoon Ha, Gabriel Nelson
Under Review

Work done during internship at the RAI Institute

Safe Navigation of Bipedal Robots via Koopman Operator-Based Model Predictive Control
Jeonghwan Kim, Yunhai Han, Harish Ravichandar, Sehoon Ha
Under Review

[Project Page] [Arxiv]

Switch4EAI: Leveraging Console Game Platform for Benchmarking Robotic Athletics
Tianyu Li, Jeonghwan Kim, Wontaek Kim, Donghoon Baek, Seungeun Rho, Sehoon Ha
CoRL Open-Source Hardware Workshop 2025

[Project] [Arxiv]

ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments
Jeonghwan Kim, Tianyu Li, Sehoon Ha
IROS 2023

[Project] [Paper] [Video]

ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters
Tianyu Li, Jungdam Won, Alexander Clegg, Jeonghwan Kim, Akshara Rai, Sehoon Ha
SIGGRAPH ASIA 2023

[Project] [Paper] [Video]

Auto-rigging 3D Bipedal Characters in Arbitrary Poses
Jeonghwan Kim, Hyeontae Son, Jinseok Bae, Young Min Kim
EUROGRAPHICS Short Paper 2021

[PDF] [Code]

Learning to generate 3D shapes with Generative Cellular Automata
Dongsu Zhang, Changwoon Choi, Jeonghwan Kim, Young Min Kim
ICLR 2021

[PDF] [Code]



Previous Projects
Implementation of PPO for Multi-Agent Path Finding with Dynamic Obstacles

Efficient multi-agent path finding algorithm is essential for reducing cost when deploying robots to logoistics warehouses. In this project, we train a Multi Agent variant of Proximal Policy Optimization(PPO) algorithm for multi agent path finding with dynamic obstacles.

[PDF]

Stabilizing Controllers with Root Based Polynomial Regression

We propose a novel method of stabilizing classical controllers via techniques from machine learning. We use Polynomial Root Kernel(PRK) and Polynomial Root Gradients(PRG) to trained neural network to generate both discrete and continuous controllers satisfying root criterion stability. We successfully generated stabilizing feed-back controllers and parallel feed-forward compensator(PFC) along with unique application to Belgian chocolate problem.

Design and Control of Scalable Magnetic Levitation System with Deep Reinforcement Learning

We Model 3DoF levitating magnetic ball with 2D plane of electro magnets on MATLAB/Simulink. The three dimensional positional control of the levitating object was done via Deep Deterministic Policy Gradient (DDPG) algorithm.

[PDF]




Teaching Experience
CS4496/7496 Computer Animation
[Spline Visualizer] [Vector Field Visualizer]
CS3451 Computer Graphics

The site is generated using template from Maks Sorokin