Localization, inspection, and reasoning (LIRA) module for autonomous workflows in self-driving laboratories

Self-driving labs (SDLs) combine robotic automation with artificial intelligence (AI) to allow autonomous, high-throughput experimentation. However, robot manipulation in most SDL workflows operates in an open-loop manner, lacking real-time error detection and error correction. This can reduce reliability and overall efficiency. Here, we introduce LIRA (Localization, Inspection, and Reasoning), which is an edge computing module that enhances robotic decision-making through vision-language models (VLMs). LIRA enables precise localization, automated error inspection, and reasoning, thus allowing robots to adapt dynamically to variations from the expected workflow state. Integrated within a client-server framework, LIRA supports remote vision inspection and seamless multi-platform communication, improving workflow flexibility. Through extensive testing, LIRA achieves high localization accuracy, a tenfold reduction in localization time, and real-time inspection across diverse tasks, increasing the efficiency and robustness of autonomous workflows considerably. As an open-source solution, LIRA facilitates AI-driven automation in SDLs, advancing autonomous, intelligent, and resilient laboratory environments. Longer term, this will accelerate scientific discoveries through more seamless human-machine collaboration.

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Publication Details

Journal
Communications Chemistry
Published
2025-11-28
DOI
https://doi.org/10.1038/s42004-025-01770-1
Citations
2
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
8.34

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article

Localization, inspection, and reasoning (LIRA) module for autonomous workflows in self-driving laboratories

Hatem Fakhruldeen, Satheeshkumar Veeramani, Francisco Munguia‐Galeano, Andrew I. Cooper et al.
2 citations
Communications Chemistry
Scientific Computing and Data Management
8.34
article

Localization, inspection, and reasoning (LIRA) module for autonomous workflows in self-driving laboratories

Hatem Fakhruldeen, Satheeshkumar Veeramani, Francisco Munguia‐Galeano, Andrew I. Cooper, Zhengxue Zhou
article en
2 citations

Abstract

Self-driving labs (SDLs) combine robotic automation with artificial intelligence (AI) to allow autonomous, high-throughput experimentation. However, robot manipulation in most SDL workflows operates in an open-loop manner, lacking real-time error detection and error correction. This can reduce reliability and overall efficiency. Here, we introduce LIRA (Localization, Inspection, and Reasoning), which is an edge computing module that enhances robotic decision-making through vision-language models (VLMs). LIRA enables precise localization, automated error inspection, and reasoning, thus allowing robots to adapt dynamically to variations from the expected workflow state. Integrated within a client-server framework, LIRA supports remote vision inspection and seamless multi-platform communication, improving workflow flexibility. Through extensive testing, LIRA achieves high localization accuracy, a tenfold reduction in localization time, and real-time inspection across diverse tasks, increasing the efficiency and robustness of autonomous workflows considerably. As an open-source solution, LIRA facilitates AI-driven automation in SDLs, advancing autonomous, intelligent, and resilient laboratory environments. Longer term, this will accelerate scientific discoveries through more seamless human-machine collaboration.

Communications ChemistryVol. 8(1)
University of Liverpool (GB), Leverhulme Trust (GB)
Innovative Research Group Project of the National Natural Science Foundation of China, Leverhulme Trust, Leverhulme Research Centre for Functional Materials Design, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 2%
Scientific Computing and Data Management
8.34
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