Min-Su Lee

Seoul National University, Hanyang University

Papers

Total Citations

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About

Min-Su Lee is a pioneering researcher at the intersection of robotics, reinforcement learning, and bio-inspired materials. His work centers on developing intelligent robotic systems capable of complex, creative manipulation tasks and designing novel surface structures for enhanced robotic interaction. Lee's most notable contribution is his 2022 paper "From Scratch to Sketch," which introduces a deep decoupled hierarchical reinforcement learning framework for a robotic sketching agent. This work, garnering 10 citations, is groundbreaking for enabling a robot to simultaneously learn stroke-based rendering and motor control from scratch, effectively bridging the gap between high-level artistic intent and low-level physical actuation. The hierarchical approach decouples the planning of drawing strokes from the precise motor commands, allowing for more efficient and adaptable learning. More recently, in 2024, Lee has explored robust design principles for elastomeric surfaces with dimpled pillar patterns, achieving directional friction control for robot grippers. This work, with 4 citations, demonstrates his versatility in applying materials science to solve practical challenges in robotic manipulation. Lee's research is highly relevant for advancing autonomous systems in creative industries and improving dexterous robotic grasping.