Minimal Embodiment Enables Efficient Learning of Number Concepts in Robot
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 th...
| Authors | Zhegong Shangguan, Alessandro Di Nuovo, Angelo Cangelosi |
|---|---|
| Primary category | cs.RO |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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