# Minimal Embodiment Enables Efficient Learning of Number Concepts in Robot

arXiv: https://arxiv.org/abs/2604.11373 · HTML: https://mlsanity.com/paper/2604.11373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 86 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Core Knowledge Deficits in Multi-Modal Language Models](https://mlsanity.com/paper/2410.10855/) (2024)
- [Taming the Panda with Python: A Powerful Duo for Seamless Robotics Programming and Integration](https://mlsanity.com/paper/2307.07633/) (2023)
- [Zero-shot counting with a dual-stream neural network model](https://mlsanity.com/paper/2405.09953/) (2024)
- [A robot that counts like a child: a developmental model of counting and pointing](https://mlsanity.com/paper/2008.02366/) (2020)
- [Influence of Pointing on Learning to Count: A Neuro-Robotics Model](https://mlsanity.com/paper/1907.05269/) (2019)
- [RNNs perform task computations by dynamically warping neural representations](https://mlsanity.com/paper/2512.04310/) (2025)

## Abstract (arXiv)

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 = 30.6deg/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.