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BioAgent: Self-Directed Skill Acquisition and Capability Growth for Long-Horizon Laboratory Protocols — arXiv:2610.41383

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41383
License (as archived): CC0-1.0
Archived: 2026-11-03T05:47:23.645Z
SHA-256 of the archived text: 785c7a189de0d7c58b0c44ca6f4a0a86902a728b0ad0d19277ccb5619942c815
Stable URL of this page: https://metraipolis.com/documento/dc_2bc45c53700a

How to cite

BioAgent: Self-Directed Skill Acquisition and Capability Growth for Long-Horizon Laboratory Protocols — arXiv:2610.41383 — arXiv (en), https://arxiv.org/abs/2610.41383 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2bc45c53700a (Metraipolis archive dc_2bc45c53700a, sha256 785c7a189de0d7c58b0c44ca6f4a0a86902a728b0ad0d19277ccb5619942c815)

BibTeX

@misc{dc2bc45c53700a,
  title = {BioAgent: Self-Directed Skill Acquisition and Capability Growth for Long-Horizon Laboratory Protocols — arXiv:2610.41383},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41383},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2bc45c53700a; sha256 785c7a189de0d7c58b0c44ca6f4a0a86902a728b0ad0d19277ccb5619942c815},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41383. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Executing an unfamiliar laboratory protocol requires a robot to identify missing capabilities and determine whether each operation remains valid as the experiment progresses. This letter presents BioAgent, an embodied agent that addresses these coupled challenges of capability sufficiency and experimental validity. It matches human-reviewed requirements to validated skills and task packages, selects tool-based implementations for missing behaviors, and requests human teaching when needed during learning. Learned skills undergo sequential task-package validation to check outcomes and compatibility between successive operations. During execution, precondition, outcome, and experimental-state checks govern progression, autonomous recovery, or termination. Execution evidence guides tool selection, while accepted experience supports offline policy updates and task packaging. On the second task, BioAgent reuses 17 skill types unchanged, revises two existing implementations, and acquires five new types. On two dual-arm laboratory tasks with 20 and 31 ordered skill invocations, BioAgent achieves complete-task success rates of 66\% and 58\% with autonomous recovery over 50 runs per task. Across 25 atomic skills evaluated over 50 autonomous trials each with recovery disabled, success rates range from 84\% to 100\%, with 17 skills achieving 50/50.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.41383 — 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 2026-11-03T05:33:48.016Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).