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Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

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A Database for Learning Numbers by Visual Finger Recognition in Developmental Neuro-Robotics.

Sergio Davies¹, Alexandr Lucas^1,2, Carlos Ricolfe-Viala³

¹Department of Computing, Sheffield Hallam University, Sheffield, United Kingdom.

Frontiers in Neurorobotics

|March 19, 2021

Summary

This summary is machine-generated.

This study introduces a new image database of finger-based number representations for human and robot hands. This resource aids in developing realistic artificial intelligence models for numerical cognition in robots.

Keywords:

SSD cognitive robotics developmental neuro-robotics developmental robotics finger counting iCub robot region-based CNN single shot detector

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Area of Science:

Cognitive Science
Artificial Intelligence
Robotics

Background:

Numerical cognition is a key aspect of human intelligence, studied across multiple disciplines.
Current artificial intelligence models for numerical cognition lack realistic sensory-motor integration, such as finger manipulation.
Humanoid robots require grounded learning approaches for developmental autonomy.

Purpose of the Study:

To present a novel image database of finger-based number representations using human and robot hands.
To establish a benchmark for training and testing deep neural networks (DNNs) for numerical cognition models.
To enable grounded learning in developmental autonomous agents by incorporating realistic sensory-motor data.

Main Methods:

Creation of a comprehensive image database featuring finger-based number representations.
Training, validation, and testing of five state-of-the-art deep neural networks.
Benchmark analysis of dataset performance and computational requirements for each DNN.

Main Results:

The database facilitates the development of realistic numerical cognition models for humanoid robots.
Comparative analysis of DNNs reveals trade-offs between detection speed and precision.
The study provides insights into computational demands for robotic applications.

Conclusions:

The presented database is crucial for advancing grounded learning in developmental autonomous agents.
Optimizing the speed-precision trade-off is essential for practical robotic applications of numerical cognition.
This work lays the foundation for more sophisticated AI models of numerical cognition.