R. de Giuseppe

Papers

Total Citations

H-Index

About

R. de Giuseppe’s research lies at the intersection of robotics, computer vision, and intelligent control, with a primary focus on autonomous manipulation. His most influential work, “Learning to grasp by using visual information” (2003, 22 citations), tackles the fundamental challenge of enabling a robotic manipulator to identify and grasp objects using only monocular vision. In this study, de Giuseppe developed a controller that learns an optimal policy for reaching and grasping spherical objects, effectively bridging the gap between visual input and physical action. He further refined this approach in “Visual servoing of a robotic manipulator based on fuzzy logic control” (2003, 9 citations), where he replaced traditional control laws with a fuzzy logic system to approximate the grasping task. This work demonstrated how soft computing techniques could enhance the adaptability and robustness of visual servoing in real-world platforms. Though his citation counts are modest, de Giuseppe’s contributions are notable for their early integration of learning and fuzzy control into vision-based manipulation—a precursor to modern deep reinforcement learning approaches. His research remains a valuable reference for students and engineers exploring sensorimotor control in robotics.