Haeseong Lee

Papers

Total Citations

H-Index

About

Haeseong Lee is a robotics researcher whose work centers on robotic assembly, contact state estimation, and motion planning — areas at the forefront of enabling intelligent automation in unstructured environments. His most influential contribution, "Contact State Estimation for Peg-in-Hole Assembly Using Gaussian Mixture Model" (2022), has garnered 33 citations and addresses a critical challenge in robotic assembly: reliably detecting and monitoring contact states to prevent assembly failures when robots operate outside controlled, predictable settings. By leveraging Gaussian Mixture Models, Lee developed a robust estimation framework that significantly advances a robot's ability to handle real-world uncertainties. His complementary work on robotic furniture assembly (2022, 13 citations) demonstrates a broader systems-level perspective, integrating task abstraction, motion planning, and control into a unified pipeline — a notably complex challenge given the multi-step, precision-demanding nature of furniture construction. Together, these contributions reflect Lee's commitment to bridging the gap between laboratory robotics and practical deployment, making him a valuable voice for students and researchers interested in contact-rich manipulation, intelligent perception, and autonomous assembly systems.