{
  "id": 5944,
  "url": "https://arxiv.org/abs/2604.11373v1",
  "title": "Minimal Embodiment Enables Efficient Learning of Number Concepts in Robot",
  "summary": "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",
  "authors": "Zhegong Shangguan, Alessandro Di Nuovo, Angelo Cangelosi",
  "category": "research",
  "topics": "agents-autonomy,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-13T12:14:58.000Z",
  "fetched_at": "2026-07-14T16:32:06.470Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5944",
  "original_url": "https://arxiv.org/abs/2604.11373v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}