Ph.D. candidate in Ingegneria Informatica E Dei Sistemi , 41st cycle (2025-2028)
Department of Control and Computer Engineering (DAUIN)
Profile
PhD
Research topic
Robust and Scalable 3D Learning for Object-Centric Trajectory Generation in Robotic Spray Painting
Tutors
Keywords
Biography
I am a robotic engineer specializing in industrial automation and artificial intelligence. My primary career focus is bridging the gap between advanced AI theory and applied engineering to improve the flexibility, quality, and autonomy of industrial robotics.
Education and Background
I hold both a B.Sc. and an M.Sc. in Robotic Engineering from the University of Udine, completing my Master's degree in 2019. During my studies, I developed practical experience designing, training, and validating machine learning algorithms through my Master's thesis, "A Machine Vision Solution to Detect Slag in Real Continuous Steel Casting," which focused on visual anomaly detection in industrial processes.
Industry Experience
Since completing my degree, I have been working at CMA Robotics, a company that specializes in robotic painting systems. My role involves developing software that automatically generates painting trajectories from vision sensor data, effectively integrating computer vision algorithms with robotic motion generation.
Research and Future Goals
Currently, I am pursuing an Executive Doctorate in Computer and Systems Engineering at Politecnico di Torino, focusing on the fundamental challenge of object-centric, long-horizon trajectory generation in robotics. My research leverages generative AI, specifically diffusion models, to enable robots to understand complex geometries and autonomously plan optimal motions based on expert demonstrations. While these methodologies have broad potential across the robotics field, I am specifically applying them to the complex industrial task of autogenerating optimal painting trajectories directly from 3D point clouds.
Research Topics
3D object-centric trajectory generation, long-horizon planning, robotic spray painting, imitation learning, diffusion models, self-supervised learning