An embodied model for handwritten digits recognition in a cognitive robot

DI NUOVO, Alessandro (2017). An embodied model for handwritten digits recognition in a cognitive robot. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. IEEE, 1-6.

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Official URL: https://ieeexplore.ieee.org/document/8285274/
Link to published version:: https://doi.org/10.1109/SSCI.2017.8285274

Abstract

This paper presents an embodied model for recognition of handwritten digits in a cognitive developmental robot scenario. Inspired by neuro-psychological data, the model integrates three modules: a stacked auto-encoder network to process the visual information, a feedforward neural controller for the fingers, and a generalized regression network that associates number digits to finger configurations. Results from developmental learning experiments show an improvement in the digits' recognition rate thanks to the inclusion of the robot fingers in the training especially in its early stages (epochs) or with a low number of examples. This behaviour can be linked to that observed in psychological studies with children, who seem to benefit of finger counting only in the initial stage of mathematical learning. These results suggest the potential of the embodied approach to favour the creation of a psychologically plausible developmental model for mathematical cognition in robots and to support the creation of more complex models of human-like behaviours.

Item Type: Book Section
Additional Information: Presented at the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 27 Nov.-1 Dec. 2017, IEEE. INSPEC Accession Number: 17560522 IEEE Catalog Number: CF17COI-USB
Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Materials and Engineering Research Institute > Centre for Automation and Robotics Research > Mobile Machine and Vision Laboratory
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
Identification Number: https://doi.org/10.1109/SSCI.2017.8285274
Depositing User: Alessandro Di Nuovo
Date Deposited: 09 May 2018 13:17
Last Modified: 15 May 2018 21:24
URI: http://shura.shu.ac.uk/id/eprint/20886

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