Laboratory Automation

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Overview

Laboratory automation studies how to make experimental work—from preparing samples and operating instruments to collecting and analyzing data—more repeatable and less dependent on manual effort. Approaches include scripted orchestration and instrument interfaces, language-model agents that translate protocols into actions, vision-language-action robot policies, and closed-loop control using camera or sensor feedback. Self-driving labs use these tools to run iterative experiments, while simulation platforms and benchmarks test manipulation, protocol execution, and safety before or alongside real-world trials. A central challenge is ensuring that systems are safe and precise across unfamiliar equipment and long workflows: completing a task does not necessarily mean the experiment met its scientific tolerances or produced valid results.

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62 papers9 papers in the last four weeks, up 29% on the four weeks before. 0.1% of all new papers.

  1. EAA: Automating materials characterization with vision language model agents

    Feb 17, 2026Ming Du, Yanqi Luo, Srutarshi Banerjee +3Laboratory AutomationTool-Using Vision-Language Agents

    Advanced Photon Source Argonne National Laboratory Lemont, IL, USA · Data Science and Learning Division Argonne National Laboratory Lemont, IL, USA · Department of Radiation Oncology Northwestern University Chicago, IL, USA

  2. Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation

    Aug 7, 2025Kartar Kumar, Rajesh Kumar, Nikesh LagunAI-Assisted Scientific ResearchLaboratory Automation

    Department of Computer Science, Institute of Business Administration (IBA), Karachi, Pakistan · International Research Center for Complexity Sciences, Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China · School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China