Toward Full Autonomous Laboratory Instrumentation Control with Large Language Models
- URL: http://arxiv.org/abs/2604.03286v1
- Date: Wed, 25 Mar 2026 22:01:45 GMT
- Title: Toward Full Autonomous Laboratory Instrumentation Control with Large Language Models
- Abstract summary: Large language models (LLMs) can enable efficient programming and automation of scientific equipment.<n>We show how ChatGPT can facilitate the creation of custom scripts for instrumentation control, significantly reducing the technical barrier for experimental customization.<n>This approach underscores the transformative role of LLM-based tools and AI agents in democratizing laboratory automation and accelerating scientific progress.
- Score: 2.442497996114172
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The control of complex laboratory instrumentation often requires significant programming expertise, creating a barrier for researchers lacking computational skills. This work explores the potential of large language models (LLMs), such as ChatGPT, and LLM-based artificial intelligence (AI) agents to enable efficient programming and automation of scientific equipment. Through a case study involving the implementation of a setup that can be used as a single-pixel camera or a scanning photocurrent microscope, we demonstrate how ChatGPT can facilitate the creation of custom scripts for instrumentation control, significantly reducing the technical barrier for experimental customization. Building on this capability, we further illustrate how LLM-assisted tools can be extended into autonomous AI agents capable of independently operating laboratory instruments and iteratively refining control strategies. This approach underscores the transformative role of LLM-based tools and AI agents in democratizing laboratory automation and accelerating scientific progress.
Related papers
- Sci-VLA: Agentic VLA Inference Plugin for Long-Horizon Tasks in Scientific Experiments [49.02509634515056]
Recent vision-language-action models offer a promising foundation for robotic laboratories.<n>Experiments typically involve long-horizon tasks composed of multiple atomic tasks.<n>While VLA models fine-tuned for scientific tasks can reliably execute atomic experimental actions, they often fail to perform composite tasks formed by reordering and composing these known atomic actions.
arXiv Detail & Related papers (2026-02-10T05:50:19Z) - Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale [82.20980951765891]
We argue that scaling agentic science requires an infrastructure-and-ecosystem approach, instantiated Bohrium+SciMaster.<n>Bohrium acts as a managed, traceable hub for AI4S assets that turns diverse scientific data, software, compute, and laboratory systems into agent-ready capabilities.<n>SciMaster orchestrates these capabilities into long-horizon scientific, on which scientific agents can be composed and executed.
arXiv Detail & Related papers (2025-12-23T16:04:41Z) - An Agentic Framework for Autonomous Materials Computation [70.24472585135929]
Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery.<n>Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific experiments.<n>Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations.
arXiv Detail & Related papers (2025-12-22T15:03:57Z) - SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
arXiv Detail & Related papers (2025-11-29T09:18:39Z) - BioMARS: A Multi-Agent Robotic System for Autonomous Biological Experiments [8.317138109309967]
Large language models (LLMs) and vision-language models (VLMs) have the potential to transform biological research by enabling autonomous experimentation.<n>Here we introduce BioMARS, an intelligent platform that integrates LLMs, VLMs, and modular robotics to autonomously design, plan, and execute biological experiments.<n>A web interface enables real-time human-AI collaboration, while a modular backend allows scalable integration with laboratory hardware.
arXiv Detail & Related papers (2025-07-02T08:47:02Z) - An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework [49.633199780510864]
This work proposes a multi-agent autonomous mechatronics design framework, integrating expertise across mechanical design, optimization, electronics, and software engineering.<n> operating primarily through a language-driven workflow, the framework incorporates structured human feedback to ensure robust performance under real-world constraints.<n>A fully functional autonomous vessel was developed with optimized propulsion, cost-effective electronics, and advanced control.
arXiv Detail & Related papers (2025-04-20T16:57:45Z) - LLM Agents Making Agent Tools [2.5529148902034637]
Tool use has turned large language models (LLMs) into powerful agents that can perform complex multi-step tasks.<n>But these tools must be implemented in advance by human developers.<n>We propose ToolMaker, an agentic framework that autonomously transforms papers with code into LLM-compatible tools.
arXiv Detail & Related papers (2025-02-17T11:44:11Z) - VISION: A Modular AI Assistant for Natural Human-Instrument Interaction at Scientific User Facilities [0.19736111241221438]
generative AI presents an opportunity to bridge this knowledge gap.<n>We present a modular architecture for the Virtual Scientific Companion (VISION)<n>With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an X-ray scattering beamline.
arXiv Detail & Related papers (2024-12-24T04:37:07Z) - Autonomous Microscopy Experiments through Large Language Model Agents [4.241267255764773]
Large language models (LLMs) are revolutionizing self driving laboratories (SDLs) for materials research.<n>We introduce Artificially Intelligent Lab Assistant (AILA), a framework automating atomic force microscopy through LLM driven agents.<n>We find that state of the art models struggle with basic tasks and coordination scenarios.
arXiv Detail & Related papers (2024-12-18T09:35:28Z) - Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents [56.822238860147024]
Augmenting large language models with external tools has emerged as a promising approach to extend their utility.<n>Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning.<n>We propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.
arXiv Detail & Related papers (2024-05-26T11:40:58Z) - From Language Models to Practical Self-Improving Computer Agents [0.8547032097715571]
We develop a methodology to create AI computer agents that can carry out diverse computer tasks and self-improve.
We prompt an LLM agent to augment itself with retrieval, internet search, web navigation, and text editor capabilities.
The agent effectively uses these various tools to solve problems including automated software development and web-based tasks.
arXiv Detail & Related papers (2024-04-18T07:50:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.