I am a researcher in robot design optimization. I am a member of the Robot Intelligence Lab (RILAB) at Korea University.
I received my bachelor’s degree from Korea University in 2024.
My current research interests include humanoid kinematics and learning-based design optimization.
Learns a compact, geometry-preserving latent space of humanoid upper-body designs from existing mechanical designs and uses human motion data - via motion retargeting and Procrustes analysis - as the optimization objective, enabling automated discovery of new robot kinematics with minimal human design input.
Social navigation framework that learns a density-based reward from positive and negative demonstrations and augments it with rule-based objectives for obstacle avoidance and goal reaching. A sampling-based lookahead controller produces safe yet adaptive supervisory actions, which are distilled into a compact student policy suitable for real-time operation with uncertainty estimates.
Hierarchical vision-language-action model that learns from both success and failure demonstrations: a high-level System 2 performs feasibility-guided tree search over scene-graph subgoals using success/failure probabilities, while a low-level System 1 executes the selected actions - turning failure data into a structured signal for robust manipulation.
An automated pipeline that converts text-generated human motions into physically executable humanoid motions: text-to-motion generation, robot-specific retargeting, optimization-based motion refinement, and a contact-aware RL phase - reducing foot sliding, unnatural floating, and excessive joint accelerations before RL training.