Article Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation

Ryo Tamura SAMURAI ORCID ; Hiromichi Taketa ; Satoshi Murata ; Daisuke Ryuno ; Tomotaka Yokota ; Koji Tsuda SAMURAI ORCID ; Shoichi Matsuda SAMURAI ORCID

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO a case study on liquid handler automation.pdf
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Ryo Tamura, Hiromichi Taketa, Satoshi Murata, Daisuke Ryuno, Tomotaka Yokota, Koji Tsuda, Shoichi Matsuda. Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation. Science and Technology of Advanced Materials: Methods. 2025, 5 (1), 2565144. https://doi.org/10.1080/27660400.2025.2565144

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(abstract)

The orchestration software (OS) for controlling self-driving laboratories (SDLs) has been advanced significantly in recent years. We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. In this study, we demonstrate the realization of an SDL via NIMO by integrating AI into a legacy robotic system. As a proof of concept, we integrated an automated liquid handling system controlled by a Visual Basic (VB) program into the SDL through NIMO and performed parameter optimization of the dispensing process using Bayesian optimization, thereby enabling autonomous and automated experiments. NIMO facilitates AI integration through straightforward file exchanges, ensuring compatibility with robotic experimental systems programmed in non-Python languages such as VB and LabVIEW, as well as SDLs managed by other OS platforms. We anticipate that NIMO’s ability to support a broad spectrum of AI-driven autonomous experiments will significantly enhance the functionality and versatility of SDLs.

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Keyword: Self-driving laboratories, NIMO

Date published: 2025-12-31

Publisher: Informa UK Limited

Journal:

  • Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 5 issue. 1 2565144

Funding:

  • Ministry of Education, Culture, Sports, Science, and Technology (MEXT) Program
  • Data Creation and Utilization Type Materials Research and Development Project JPMXP1121467561
  • JST, PRESTO JPMJPR24T8

Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1080/27660400.2025.2565144

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Updated at: 2025-11-28 08:30:14 +0900

Published on MDR: 2025-11-28 08:22:39 +0900

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