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About

Jaesoon Lee is a robotics researcher whose work focuses on advancing the design and control of humanoid and collaborative robotic systems. His primary research areas include bio-inspired robot design, optimal trajectory generation, and manipulability optimization for enhanced robotic dexterity and safety. Lee’s major contribution lies in developing novel approaches to improve robot versatility and workspace. Notably, his 2023 paper on "Design of 9-DOF humanoid arms inspired by the human’s inner shoulder" (12 citations) introduces a groundbreaking architecture that mimics human shoulder biomechanics to significantly expand a robot’s range of motion and adaptability. This work has been recognized for its potential to bridge the gap between human-like flexibility and robotic precision. Additionally, Lee has pioneered a Differential Dynamic Programming (DDP)-based trajectory generation method that integrates manipulability measures, enabling 6-DOF collaborative robots to autonomously avoid singularities while maintaining smooth, efficient motion. His 2024 paper on this topic (3 citations) represents a key step toward safer, more capable human-robot collaboration. With a growing citation record and a clear focus on practical, bio-inspired solutions, Lee is establishing himself as an emerging leader in the field of robotic manipulation and control.