Myoung‐Su Choi
Papers
6
Total Citations
87
H-Index
3
About
Myoung-Su Choi is a leading researcher in intelligent robotic assembly, with a focus on bridging the gap between human dexterity and autonomous manufacturing. His work centers on three key areas: dual-arm manipulation, deep reinforcement learning (RL) for industrial tasks, and brain-computer interfaces for robotic control. Choi’s most impactful contribution is his human-centric approach to the classic “peg-in-hole” assembly problem, as detailed in his top-cited paper (52 citations), where he employs dual-arm robots and dexterous hands to mimic human coordination. He further advances this with a novel kinesthetic sensing method using a three-finger gripper to locate holes without vision. To optimize robot learning under real-world uncertainty, Choi developed an adaptive discount factor for deep RL (24 citations), significantly improving training stability. His practical innovations include a screwdriving gripper that performs two-handed assembly tasks with a single arm, and he led Team SK²Y to victory in the 2021 Furniture Assembly AI-Robot Challenge. Demonstrating visionary breadth, Choi also pioneered an EEG-controlled tele-grasping system for undefined objects, enabling human-in-the-loop control for unstructured environments. His work is defining the next generation of flexible, intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
- 1Peg-in-Hole Assembly With Dual-Arm Robot and Dexterous Robot Hands52 citations · 2022
- 2
- 3Screwdriving Gripper That Mimics Human Two-Handed Assembly Tasks5 citations · 2022
- 4
- 5EEG-controlled tele-grasping for undefined objects2 citations · 2023
- 6Kinesthetic sensing of hole position by 3-finger gripper2 citations · 2020