# Influence of Pointing on Learning to Count: A Neuro-Robotics Model

## TL;DR

This paper introduces a neuro-robotics model that uses gestures, specifically pointing, to enhance learning to count, demonstrating behavior similar to human children and exploring different neural and training configurations.

## Contribution

It presents a novel neuro-robotics model that incorporates gesture-based learning and a new training procedure, advancing understanding of gesture influence on counting skills.

## Key findings

- Model's behavior aligns with children's performance patterns
- Gesture modality impacts learning efficiency
- Proposed training improves counting accuracy

## Abstract

In this paper a neuro-robotics model capable of counting using gestures is introduced. The contribution of gestures to learning to count is tested with various model and training conditions. Two studies were presented in this article. In the first, we combine different modalities of the robot's neural network, in the second, a novel training procedure for it is proposed. The model is trained with pointing data from an iCub robot simulator. The behaviour of the model is in line with that of human children in terms of performance change depending on gesture production.