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  KEYWORD

Self-supervised learning and computer vision for robotic tasks

azienda Thesis in external company    


keywords ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, MOBILE ROBOTICS

Reference persons FABRIZIO LAMBERTI

Research Groups DAUIN - GR-09 - GRAphics and INtelligent Systems - GRAINS

Thesis type THESIS WITH A COMPANY

Description Two thesis proposals in collaboration with Reply Spa are available in the context of self-supervised learning (SSL) and computer vision applied to mobile robots. In particular, the aim of the first thesis work is to develop new methods of SSL in computer vision in order to minimize the bottlenecks and problems of the classical methods of supervised and unsupervised learning. The goal is to adopt and improve an SSL model and then insert it into a real use case in industry or customer.

References:
Masked Siamese Networks for Label-Efficient Learning
https://arxiv.org/abs/2204.07141
SSL method DINO
https://github.com/facebookresearch /dino


The objective of the second thesis work is to apply computer vision models to robotic agents such as Spot, by Boston Dynamics, or parcel delivery robots and other IOT devices. The difficulty of this work lies in being able to compress the power of the SSL models into lighter models capable of giving excellent results having scarce reserves both hardware and connectivity.

References:
Tensorflow lite
https://www.tensorflow.org/lite
Robot Operating System
https://www.ros.org/

See also  http://grains.polito.it/work.php


Deadline 31/07/2024      PROPONI LA TUA CANDIDATURA




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