A Robot that Counts Like a Child: A Developmental Model of Counting and Pointing

Leszek Pecyna, Angelo Cangelosi, Alessandro Di Nuovo

Research output: Contribution to journalArticlepeer-review


In this paper, a novel neuro-robotics model capable of counting
real items is introduced. The model allows us to investigate the interaction
between embodiment and numerical cognition. This is composed of a deep
neural network capable of image processing and sequential tasks performance,
and a robotic platform providing the embodiment - the iCub humanoid robot.
The network is trained using images from the robot's cameras and proprioceptive signals from its joints. The trained model is able to count a set of
items and at the same time points to them. We investigate the in
uence of pointing on the counting process and compare our results with those from studies with children. Several training approaches are presented in this paper all of them uses pre-training routine allowing the network to gain the ability of pointing and number recitation (from 1 to 10) prior to counting training. The impact of the counted set size and distance to the objects are investigated. The obtained results on counting performance show similarities with those from human studies.
Original languageEnglish
JournalPsychological Research
Publication statusAccepted/In press - 23 Sept 2020


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