{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:19:23Z","timestamp":1792790363367,"version":"4.5.0"},"posted":{"date-parts":[[2026,9,18]]},"group-title":"Developmental Biology","reference-count":66,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Engineer Research and Development Center \u2013 Cold Regions Research and Engineering Laboratory","award":["W913E524C0012"],"award-info":[{"award-number":["W913E524C0012"]}],"id":[{"id":"https:\/\/ror.org\/05wk0m864","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Biological systems respond to a wide range of physical inputs and an overarching goal of biological research is understanding how these signaling events coordinate cellular, tissue, and organism-level outcomes. Yet the vast combinatorial space of physical and chemical interventions that influence these processes remains largely unexplored. Thus, a search process is required that efficiently learns how inputs affect a biological target via a series of automatically generated interventions, and a robot scientist that can conduct them. To this end we here introduce the Multimodal Organismal Modulation Robot (MOMbot), a robot scientist that integrates four physical intervention modalities\u2014chemical delivery, electrical field application, mechanical vibration, and thermal modulation\u2014within a single hardware platform. MOMbot\u2019s multimodal hardware is paired with high-resolution imaging and an online active learning algorithm that autonomously generates, executes, and refines interventions based on accumulated data. We validate the system across biological materials spanning three orders of magnitude in scale, including\n                  <jats:italic>Xenopus laevis<\/jats:italic>\n                  embryos, motile mucociliary organoids, and disaggregated ectodermal stem cells, to show the utility of this approach across diverse biological disciplines. Using these biological models, we demonstrate precise thermal control of embryonic developmental rate, automated chemical perturbation with behavioral readouts, programmable electrical field stimulation, and vibrational assembly of functional mucociliary organoids from loose cells. We also describe the active learning algorithm that autonomously concentrates interventions near regions of maximal outcome uncertainty, yielding sample-efficient discovery of an electrical duration\u2013response relationship for motile organoids. These results establish MOMbot as a scalable robot\/AI scientist team for biological discovery, enabling autonomous, multimodal physical experimentation and AI-driven exploration of complex biological systems.\n                <\/jats:p>","DOI":"10.64898\/2026.09.11.751076","type":"posted-content","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T16:30:13Z","timestamp":1789749013000},"source":"Crossref","is-referenced-by-count":0,"title":["A Closed-Loop Robot Scientist for Autonomous Biological Discovery"],"prefix":"10.64898","author":[{"given":"Kameron","family":"Bielawski","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT, USA"},{"name":"Vermont Complex Systems Institute, Burlington VT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nate","family":"Gaylinn","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shawn","family":"Beaulieu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Brucker","sequence":"additional","affiliation":[{"name":"Department of Biology, Tufts University, Medford MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Levin","sequence":"additional","affiliation":[{"name":"Department of Biology, Tufts University, Medford MA, USA"},{"name":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeantine","family":"Lunshof","sequence":"additional","affiliation":[{"name":"Department of Genetics, Harvard Medical School, Boston MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Bongard","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT, USA"},{"name":"Vermont Complex Systems Institute, Burlington VT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas","family":"Blackiston","sequence":"additional","affiliation":[{"name":"Department of Biology, Tufts University, Medford MA, USA"},{"name":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026092218401059000_2026.09.11.751076v1.1","first-page":"493","article-title":"Accurate Structure Prediction of Biomolecular Interactions with AlphaFold 3","volume":"630","year":"2026","journal-title":"Nature"},{"key":"2026092218401059000_2026.09.11.751076v1.2","doi-asserted-by":"publisher","DOI":"10.1038\/nature02236"},{"key":"2026092218401059000_2026.09.11.751076v1.3","first-page":"265","article-title":"Fully Automated Laboratory Robotic System for Automating Sample Preparation and Analysis to Reduce Cost and Time in Drug Development Process","volume":"13","year":"2008","journal-title":"JALA: Journal of the Association for Laboratory Automation"},{"key":"2026092218401059000_2026.09.11.751076v1.4","first-page":"22858","article-title":"Autonomous Discovery in the Chemical Sciences Part I: Progress","volume":"59","year":"2024","journal-title":"Angewandte Chemie International Edition"},{"key":"2026092218401059000_2026.09.11.751076v1.5","first-page":"237","article-title":"A mobile robotic chemist","volume":"583","year":"2025","journal-title":"Nature"},{"key":"2026092218401059000_2026.09.11.751076v1.6","first-page":"1403","article-title":"AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning","volume":"14","year":"2024","journal-title":"Nature Communications"},{"key":"2026092218401059000_2026.09.11.751076v1.7","first-page":"25890","article-title":"Fast machine-learning online optimization of ultra-cold-atom experiments","volume":"6","year":"2025","journal-title":"Scientific Reports"},{"key":"2026092218401059000_2026.09.11.751076v1.8","doi-asserted-by":"crossref","first-page":"7605","DOI":"10.1021\/acsnano.1c11118","article-title":"Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors. eng","volume":"16","year":"2022","journal-title":"ACS nano"},{"key":"2026092218401059000_2026.09.11.751076v1.9","doi-asserted-by":"crossref","unstructured":"Xue, D. , et al. 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