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Updated: Jan 10, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Localization, inspection, and reasoning (LIRA) module for autonomous workflows in self-driving laboratories.

Zhengxue Zhou1,2, Satheeshkumar Veeramani1, Francisco Munguia-Galeano1

  • 1Department of Chemistry and Materials Innovation Factory, University of Liverpool, Liverpool, United Kingdom.

Communications Chemistry
|November 29, 2025
PubMed
Summary
This summary is machine-generated.

Self-driving labs (SDLs) now feature enhanced robotic decision-making with LIRA (Localization, Inspection, and Reasoning). This AI module improves reliability and efficiency in autonomous experimentation by enabling real-time error correction.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Laboratory Automation

Background:

  • Self-driving labs (SDLs) utilize robotic automation and AI for high-throughput experimentation.
  • Current SDL workflows often lack real-time error detection and correction in robot manipulation, reducing reliability and efficiency.

Purpose of the Study:

  • To introduce LIRA (Localization, Inspection, and Reasoning), an edge computing module designed to enhance robotic decision-making in SDLs.
  • To enable robots to dynamically adapt to workflow variations through precise localization, automated inspection, and reasoning.

Main Methods:

  • Integration of LIRA with a client-server framework for remote vision inspection and multi-platform communication.
  • Utilizing vision-language models (VLMs) within LIRA for enhanced robotic perception and decision-making.

Main Results:

  • LIRA demonstrated high localization accuracy and a tenfold reduction in localization time.
  • Achieved real-time inspection capabilities across diverse experimental tasks, significantly improving autonomous workflow efficiency and robustness.
  • LIRA operates as an open-source solution, promoting accessibility and adoption.

Conclusions:

  • LIRA enhances the reliability, efficiency, and flexibility of autonomous workflows in self-driving labs.
  • Facilitates AI-driven automation, advancing towards intelligent and resilient laboratory environments.
  • Aims to accelerate scientific discovery through improved human-machine collaboration.