A. Anglani

Istituto Nazionale di Fisica Nucleare, Sezione di Lecce

Papers

Total Citations

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About

A. Anglani is a researcher whose work sits at the compelling intersection of robotics, computer vision, and intelligent control systems. Their most significant contributions focus on the challenge of robotic manipulation — specifically, enabling robotic arms to visually identify and successfully grasp target objects in real-world environments. Anglani's most cited work, "Learning to grasp by using visual information" (2003, 22 citations), demonstrates an innovative approach to teaching robotic systems optimal grasping policies through reinforcement learning combined with monocular vision, deployed on physical robotic platforms rather than mere simulations. This commitment to real-world applicability distinguishes their research approach. Complementing this, their earlier work on Q-learning controllers for target reaching (2000, 17 citations) laid important theoretical groundwork for adaptive robotic navigation using visual feedback. Anglani also explored fuzzy logic as an alternative control paradigm for visual servoing tasks, broadening the toolkit available for manipulation problems. Collectively, their research has helped advance the field of autonomous robotic grasping, contributing foundational methods that bridge machine learning, fuzzy systems, and practical robotics engineering — areas that remain critically relevant in today's rapidly evolving robotics landscape.