Long-short term memory networks for modelling embodied mathematical cognition in robots

DI NUOVO, Alessandro (2018). Long-short term memory networks for modelling embodied mathematical cognition in robots. In: World Congress on Computational Intelligence 2018, Rio de Janeiro, 8-13 July. IEEE.

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Abstract

Mathematical competence can endow robots with the necessary capability for abstract and symbolic processing, which is required for higher cognitive functions such as natural language understanding. But, so far, only few attempts have been made to model mathematical cognition in robots. This paper presents an experimental evaluation of the Long-Short Term Memory networks for modeling the simple mathematical operation of single-digits addition in a cognitive robot. To this end, the robotic model creates an association between the proprioceptive information from finger counting and the handwritten digits of the MNIST dataset. In practice, the model executes two tasks concurrently: it recognizes the handwritten digits in a sequence and sums them. The results show that the association with fingers can improve the robot precision, as observed in children. Also, the robot makes a disproportionate number of split-five errors similarly to what observed in studies with children and adults, hence giving evidence to support the hypothesis that these errors are due the use of a five-fingers counting system.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Electronic ISSN: 2161-4407
Research Institute, Centre or Group: 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: Faculty of Science, Technology and Arts > Department of Computing
Related URLs:
Depositing User: Alessandro Di Nuovo
Date Deposited: 05 Jun 2018 08:58
Last Modified: 30 Nov 2018 10:00
URI: http://shura.shu.ac.uk/id/eprint/20884

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