ETD system

Electronic theses and dissertations repository

 

Tesi etd-04112018-214844


Thesis type
Tesi di laurea magistrale
Author
FRATI, LAPO
URN
etd-04112018-214844
Title
Vision-based Deep Learning Model for Guiding Multi-fingered Robotic Grasping
Struttura
INFORMATICA
Corso di studi
INFORMATICA
Commissione
relatore Dott. Bacciu, Davide
relatore Prof. Bicchi, Antonio
relatore Dott. Bianchi, Matteo
Parole chiave
  • machine learning
  • deep learning
  • computer vision
  • robotic grasping
Data inizio appello
27/04/2018;
Consultabilità
completa
Riassunto analitico
Grasping is an area where humans still vastly outperform robots. By leveraging recent advances in deep learning we propose a vision-based model to generate human-inspired sequences of grasping primitives suitable for transfer to multi-fingered robotic hands. The proposed model, inspired by Neural Image Captioning, consists of a convolutional and recurrent part. The convolutional part employs a pre-trained model from ILSVRC-2014 adapted to combine features from multiple points of view of a single object by using a view pooling layer. The extracted features are then used to seed Long Short Term Memory recurrent units and generate sequences of primitives that can be used to guide a sophisticated multi-fingered robotic hand during the approach leading to a grasp.
File