Metraipolis › archive › dc_e93e20ef4e22

Embodied AI for Scientific Discovery: From Physical Action to Scientific Evidence — arXiv:2611.04371

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04371
License (as archived): CC0-1.0
Archived: 2026-11-04T12:27:51.003Z
SHA-256 of the archived text: 319213bc98f0a781744eb822980df345721b5bbea0cbb1ae9be6cb382616257e
Stable URL of this page: https://metraipolis.com/documento/dc_e93e20ef4e22

How to cite

Embodied AI for Scientific Discovery: From Physical Action to Scientific Evidence — arXiv:2611.04371 — arXiv (en), https://arxiv.org/abs/2611.04371 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e93e20ef4e22 (Metraipolis archive dc_e93e20ef4e22, sha256 319213bc98f0a781744eb822980df345721b5bbea0cbb1ae9be6cb382616257e)

BibTeX

@misc{dce93e20ef4e22,
  title = {Embodied AI for Scientific Discovery: From Physical Action to Scientific Evidence — arXiv:2611.04371},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04371},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e93e20ef4e22; sha256 319213bc98f0a781744eb822980df345721b5bbea0cbb1ae9be6cb382616257e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04371. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Scientific experiments are increasingly selected, executed and interpreted by learned systems that act through physical interfaces. Robot learning builds physical competence, whereas AI for science chooses which experiment to run, each reporting different endpoints. This review treats their interface as one problem, embodied AI for science, where the experiment itself is partially observed: specimen state, apparatus state and measurement processes are latent, actions are costly and irreversible, and missingness depends on execution and outcome. We organize the literature by three closures operating at distinct levels: operational closure (the experiment realized is the one intended), epistemic closure (the result updates beliefs or selects subsequent experiments) and evidential closure (the record supports a claim). We analyze workflows through a record-linked evidence cycle - Record, Diagnose, Calibrate, Confirm - as analytic functions with feedback. A taxonomy crosses these closures with six delegated decisions and five embodiment classes. Our formulation separates commanded, realized and observed conditions and models diagnosis with explicit resource, risk and sample-change costs. We compare experiment-selection methods by terminal decision, review perception, protocol compilation, visuomotor policies, world models, recovery and transfer by evidence emission requirements, and compare chemistry, materials, biology, microscopy and field workflows by what their records establish. An appendix provides an evidence map of 28 workflows (26 physical, two software-boundary), characterizing validation pathways and claim confirmation requirements. We propose evaluation that separates actions, valid experiments, campaigns and confirmed claims, framing open challenges as controlled comparisons with measurable failure conditions.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.04371 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T12:26:47.149Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).