Youngjun Joo

LG (South Korea), University of Central Florida

Papers

Total Citations

H-Index

About

Youngjun Joo is a robotics researcher whose work centers on the dynamic modeling, control, and sensorless interaction of robotic systems. His key contributions lie in the development of advanced control algorithms that enable robots to interact with their environments without relying on expensive or fragile physical sensors. His most cited paper, "Sensorless Force Control Algorithm based on Momentum Observer Technique" (2021), introduces a novel approach for estimating external forces on robotic manipulators using a momentum observer, effectively replacing the need for six-axis force/torque or joint torque sensors. This work has garnered 3 citations and represents a significant step toward more robust and cost-effective robotic manipulation. Additionally, Joo's earlier research, "Dynamic modeling and control of hopping robot in planar space" (2016), demonstrates his foundational expertise in the complex dynamics of legged locomotion, specifically for hopping robots. With a total of 5 citations across his most prominent works, Joo is establishing a reputation for practical, sensor-reducing innovations that promise to lower the barrier for deploying sophisticated force control in real-world robotic applications.