<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Salazar-Villacis P</submitter><funding>RCUK | Engineering and Physical Sciences Research Council (EPSRC)</funding><funding>EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)</funding><pagination>99</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12923569</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>9(1)</volume><pubmed_abstract>Laboratory robotics is advancing from routine automation toward autonomous systems capable of intelligent decision-making and flexible execution. This perspective outlines key milestones and introduces the ADePT framework, which defines four core dimensions of robotic capability proficiency: adaptability and learning, dexterity, perception, and task complexity. We discuss future directions for self-driving laboratories, including robot-centric, end-to-end robotic integration, and collaborative human-robot environments. These scenarios highlight the importance of technological enablers and evolving regulatory paradigms. By connecting present technologies to emerging system configurations, this work offers a foundation for designing autonomous laboratory ecosystems that support scientific di</pubmed_abstract><journal>Communications chemistry</journal><pubmed_title>The ADePT framework for assessing autonomous laboratory robotics.</pubmed_title><pmcid>PMC12923569</pmcid><funding_grant_id>101057430</funding_grant_id><funding_grant_id>EP/V062077/1</funding_grant_id><pubmed_authors>Salazar-Villacis P</pubmed_authors><pubmed_authors>Benyahia B</pubmed_authors></additional><is_claimable>false</is_claimable><name>The ADePT framework for assessing autonomous laboratory robotics.</name><description>Laboratory robotics is advancing from routine automation toward autonomous systems capable of intelligent decision-making and flexible execution. This perspective outlines key milestones and introduces the ADePT framework, which defines four core dimensions of robotic capability proficiency: adaptability and learning, dexterity, perception, and task complexity. We discuss future directions for self-driving laboratories, including robot-centric, end-to-end robotic integration, and collaborative human-robot environments. These scenarios highlight the importance of technological enablers and evolving regulatory paradigms. By connecting present technologies to emerging system configurations, this work offers a foundation for designing autonomous laboratory ecosystems that support scientific di</description><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Feb</publication><modification>2026-07-16T14:50:01.081Z</modification><creation>2026-07-09T11:02:57.485Z</creation></dates><accession>S-EPMC12923569</accession><cross_references><pubmed>41720921</pubmed><doi>10.1038/s42004-026-01932-9</doi></cross_references></HashMap>