Antonio Napolitano

Antonio Napolitano joined the Intelligent Autonomous Systems Lab on October 1st, 2025 as an Off-Campus Ph.D. Student in collaboration with Procter & Gamble (P&G). During his Ph.D., Antonio will focus on Robot Learning, where he will develop and evaluate learning-based methods for autonomous robotic manipulation and laboratory automation.

Before his Ph.D., Antonio completed both his Bachelor and Master degrees in Mathematical Engineering at Politecnico di Milano, with a specialization in Statistical Learning and Data Science. He then worked at Procter & Gamble, first as a working student and later as an intern, where he focused on Computer Vision for industrial applications.

Teaching tasks between humans often do not only include the demonstration of a task, but also the correcting and guiding of the executed task. Similar ideas appear in robot learning, where combining demonstrations, corrections, and interactive feedback can significantly improve learning speed and the quality of the resulting policies. This becomes especially relevant once the desired task involves humans or dynamic laboratory environments that require adaptation and flexibility.

The research on robot learning and autonomous skill acquisition offers many challenges, including the representation of motor skills, generalization across tasks, and the segmentation of complex behaviours into reusable primitives. Automatically identifying, organizing, and sequencing such skills is an important and still highly active research topic.

Antonio is at the beginning of his Ph.D. journey and currently has no references to list.

Research Interests

Robot Learning

Key References