Minimal Embodiment Enables Efficient Learning of Number Concepts in Robot

TL;DR

This study shows that minimal embodiment in robots enables efficient learning of number concepts, with biologically plausible representations and developmental parallels to children, using less training data than vision-only models.

Contribution

Demonstrates that minimal embodiment acts as a structural prior, significantly improving data efficiency and leading to interpretable, biologically plausible numerical representations in robotic learning.

Findings

Embodied models achieve 96.8% counting accuracy with only 10% of training data.

Embodiment functions as a structural prior, not just an information source.

Number representations exhibit logarithmic tuning and mental number line organization.

Abstract

Robots are increasingly entering human-interactive scenarios that require understanding of quantity. How intelligent systems acquire abstract numerical concepts from sensorimotor experience remains a fundamental challenge in cognitive science and artificial intelligence. Here we investigate embodied numerical learning using a neural network model trained to perform sequential counting through naturalistic robotic interaction with a Franka Panda manipulator. We demonstrate that embodied models achieve 96.8\% counting accuracy with only 10\% of training data, compared to 60.6\% for vision-only baselines. This advantage persists when visual-motor correspondences are randomized, indicating that embodiment functions as a structural prior that regularizes learning rather than as an information source. The model spontaneously develops biologically plausible representations: number-selective units with logarithmic tuning, mental number line organization, Weber-law scaling, and rotational dynamics encoding numerical magnitude ($r = 0.97$, slope $= 30.6{\deg}$/count). The learning trajectory parallels children's developmental progression from subset-knowers to cardinal-principle knowers. These findings demonstrate that minimal embodiment can ground abstract concepts, improve data efficiency, and yield interpretable representations aligned with biological cognition, which may contribute to embodied mathematics tutoring and safety-critical industrial applications.