Angelo Rendiniello

University of Pavia, Italian Institute of Technology

Papers

Total Citations

107

H-Index

About

Angelo Rendiniello is a robotics researcher whose work bridges the gap between intelligent control and practical industrial automation. His primary research areas include deep reinforcement learning, human-robot collaboration, and sensor calibration for manufacturing systems. Rendiniello’s most impactful contribution is his pioneering work on collision avoidance for robotic manipulators, where he applied Deep Reinforcement Learning (DRL) to enable safe, real-time human-robot coexistence in shared workspaces. This paper has garnered 92 citations, reflecting its significance in advancing safety protocols for collaborative robotics. He has also made notable contributions to precision sensing, developing an in situ translational hand-eye calibration method for laser profile sensors using arbitrary objects, which simplifies the integration of 3D sensing into robotic workflows. Additionally, Rendiniello introduced a flexible software architecture for robotic industrial applications, fusing state-of-the-art technologies into a standalone platform to streamline the development of complete automation solutions. His work is characterized by a practical focus on deployable systems, making him a key figure in the evolution of intelligent, safe, and adaptable robotic systems for modern manufacturing environments.