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.
We present a data-driven motion planner that autoregressively generates locomotion trjaectories for complex indoor navigation scenarios.
Project Page / VideoProject Page / Paper / Video
We train a generative 3D Cellular Automata that can generate and complete 3D data represented in Voxels.
PDF / Code
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.
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.
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
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