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 ac
Record details
Published: 13 April 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (13 April 2026), “Minimal Embodiment Enables Efficient Learning of Number Concepts in Robot,” evidence record 5944, https://ethics.ai/record/5944 (originally published by arXiv).
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