Address: 245 Church Street, ENG-426, Toronto, ON, M5B 2K3
Since December 2018, I am a Postdoctoral Research Fellow at the Ryerson Multimedia Research Laboratory in Toronto. My research focuses on 6D pose estimation of objects from monocular rgb camera using deep learning.
I received my M.Sc. in Computer Science from the University of Pisa in 2010 (110/110), and my PhD in Biorobotics from the BioRobotics Institute of Scuola Superiore Sant’Anna, Pisa, in 2014 (cum laude). I was Postdoctoral Fellow at the BioRobotics Institute of Scuola Superiore Sant’Anna from 2014 to 2015, where I was involved in the subproject “SP10 – Neurorobotics platform” of the Human Brain Project (HBP). From January 2016 to November 2018 I was a Postdoctoral Fellow at the Computer Vision Laboratory (VisLab), Institute for Systems and Robotics (ISR), Lisbon. My main research interests were on visual sensory-motor predictive controllers and on learning by demonstration for humanoid robots using vision and deep neural networks.
I collaborated in a number of EU Projects (RoboSom, Human Brain Project) and my research interests are in the areas of deep neural networks, machine learning, computer vision, internal models, predictive controllers and bioinspired robotics.
If you are interested in my full CV, please download it from the following link.
|Aug 29, 2019||Our paper Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach was published on the Journal of Intelligent and Robotic Systems|
|Jul 28, 2019||Our paper Control strategies for cleaning robots in domestic applications: A comprehensive review was published on the International Journal of Advanced Robotic Systems|
|Dec 14, 2018||Started a new position as postdoctoral research fellow at Ryerson Multimedia Research Laboratory in Toronto|
|Jun 22, 2018||Our paper Autonomous table-cleaning from kinesthetic demonstrations using Deep Learning was accepted for oral presentation at ICDL-EpiRob 2018 in Tokyo|
|Apr 27, 2018||Our paper '’iCub, clean the table’’ A robot learning from demonstration approach using deep neural networks won the Best Paper Award on ICARSC 2018 conference|