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Elisa Maiettini

Post Doc
Humanoid Sensing and Perception
Research center
About

I am a Post Doctoral researcher in the Humanoid Sensing and Perception (HSP) research line. The main fields of my research are: computer vision, machine learning and humanoid robotics. The main thread of my research is the improvement of the vision systems for humanoid robots towards a more natural, efficient and intuitive learning process,  involving human robot interaction and the development of efficient algorithmic solutions.

I graduated in Software and Electronics Engineering at the University of Perugia, Italy, in 2013 and I obtained an M.D. with honors in Software and Automation Engineering at the same university in 2016. I received the Ph. D. in Bioengineering and Robotics from 2016 to 2020, at  the Istituto Italiano di Tecnologia in collaboration with the University of Genoa, under the supervision of Prof. Lorenzo Natale and Prof. Lorenzo Rosasco.

All Publications
2022
Ceola F., Maiettini E., Pasquale G., Meanti G., Rosasco L., Natale L.
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
IEEE Transactions on Robotics
Article in Press Journal
2022
Lombardi M., Maiettini E., De Tommaso D., Wykowska A., Natale L.
Toward an Attentive Robotic Architecture: Learning-Based Mutual Gaze Estimation in Human–Robot Interaction
Frontiers in Robotics and AI - Human-robot Interaction
Article Journal
2021
Ceola F., Maiettini E., Pasquale G., Rosasco L., Natale L.
Fast Object Segmentation Learning with Kernel-based Methods for Robotics
Proceedings - IEEE International Conference on Robotics and Automation, vol. 2021-May, pp. 6476-6482
Conference Paper Conference
2021
Carfi A., Patten T., Kuang Y., Hammoud A., Alameh M., Maiettini E., Weinberg A.I., Faria D., Mastrogiovanni F., Alenya G., Natale L., Perdereau V., Vincze M., Billard A.
Hand-Object Interaction: From Human Demonstrations to Robot Manipulation
Frontiers Robotics AI, vol. 8
Article Journal
2021
Grigoletto R., Maiettini E., Natale L.
Score to Learn: A Comparative Analysis of Scoring Functions for Active Learning in Robotics
Lecture Notes in Computer Science, vol. 12899 LNCS, pp. 55-67
Conference Paper Book Series