Dongwoo Park

Papers

Total Citations

H-Index

About

Dongwoo Park is a robotics researcher whose work centers on robotic manipulation and perception, with a particular focus on advancing autonomous grasping in domestic environments. His major contribution lies in developing deep learning frameworks that integrate RGB and depth data for stable object grasping, addressing the unique challenges posed by everyday items like tableware. Park’s most-cited paper, “RGBD Fusion Grasp Network with Large-Scale Tableware Grasp Dataset” (2023), introduces a novel approach to handling flat, slippery, and irregularly shaped objects—a critical yet underexplored problem in home robotics. This work not only proposes an innovative fusion network but also provides a large-scale dataset specifically designed for tableware, enabling more robust and practical grasping solutions. With 3 citations in a short time, this paper is gaining traction as a foundational resource in the field. Park’s research bridges the gap between computer vision and robotic control, offering tangible improvements for household assistants. His achievements highlight a commitment to solving real-world manipulation challenges, making his work highly relevant for students and researchers interested in service robotics, deep learning for perception, and human-robot interaction.