F. Taurisano

Istituto Nazionale di Fisica Nucleare, Sezione di Lecce

Papers

Total Citations

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About

F. Taurisano is a robotics and autonomous systems researcher whose work has made meaningful contributions to the intersection of computer vision, machine learning, and robotic manipulation control. Operating primarily in the early 2000s, Taurisano focused on solving the fundamental challenge of enabling robotic systems to visually perceive and physically interact with objects in their environment — a cornerstone problem in intelligent robotics. Their most influential work, "Learning to grasp by using visual information" (2003, 22 citations), demonstrated how reinforcement learning — specifically Q-learning — could be combined with monocular vision systems to train robotic manipulators to autonomously reach and grasp spherical targets. This research was notable for its real-platform implementation, bridging the gap between theoretical control strategies and practical robotic deployment. Complementary work explored fuzzy logic as an alternative control approximation for visual servoing tasks, reflecting a broader interest in intelligent, adaptive control methodologies. With nearly 50 cumulative citations across their key publications, Taurisano's research contributed to foundational frameworks in visually guided robot control at a time when such capabilities were still emerging. Their body of work remains relevant to researchers exploring vision-based manipulation, reinforcement learning in robotics, and autonomous grasping systems.