実験手順を柔軟に更新する実験自動化システムの開発 －ロボットによる実験の自動化をより柔軟に－

2026-03-18 理化学研究所.,筑波大学東京科学大学

さまざまな状況に柔軟に対応して実験を自律運用するソフトウエア基盤「GEMS」

＜関連情報＞

• https://www.riken.jp/press/2026/20260318_1/index.html
• https://pubs.rsc.org/en/content/articlelanding/2026/dd/d5dd00409h

GEMS：適応型実験室自動化のための決定論的有限オートマトンフレームワーク GEMS: a deterministic finite automaton framework for adaptive laboratory automation

Yuya Tahara-Arai,Akari Kato,Koji Ochiai,Kazuya Azumi,Koichi Takahashi,Genki N. Kanda and Haruka Ozaki
Digital Discovery  Published:10 Mar 2026
DOI:https://doi.org/10.1039/D5DD00409H

Abstract

Laboratory automation increasingly requires handling complex, condition-dependent protocols that combine sequential, branching, and iterative operations. Many systems use task-oriented models, where control proceeds task to task through a predefined list. These are effective for linear, static protocols but are poorly suited to adapting to changing sample conditions or representing loops and conditional branches. We introduce the General Experimental Management System (GEMS), which instead adopts a sample-centred approach, progressing state to state, with each state defining both the operations to perform and the rules for transitioning based on observations. By formalising every experimental protocol as a partially observable Markov decision process (POMDP) and expressing its deterministic execution logic as a deterministic finite automaton (DFA), GEMS can represent heterogeneous workflow structures within a single, coherent framework and enable direct compilation into instrument-executable workflows. Its architecture includes a hierarchical experiment model, a penalty-aware scheduler combining a greedy baseline with simulated annealing refinement, and a file-based interface for instrument-agnostic control. We demonstrate GEMS in two contrasting cases: (i) fully automated Bayesian optimisation of liquid mixtures using a pipetting robot and imaging, and (ii) dynamic, long-term scheduling of multiple mammalian cell cultures executed by a LabDroid robot with autonomous imaging, passaging, medium exchange, and fault recovery. In both, GEMS maintained protocol constraints while adapting schedules in real time, showing that a state-to-state, sample-centred model provides an abstraction that maintains protocol constraints while adapting schedules in real time across heterogeneous workflows.