Minimal Embodiment Enables Efficient Learning of Number Concepts in Robot

ArXi:2604.11373v1 Announce Type: cross 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 nstrate that embodied models achieve 96.8\% counting accuracy with only 10\% of.