BioMARS Biological Multi-Agent Robotic System
An intelligent platform integrating Large Language Models (LLMs), Vision–Language Models (VLMs), and modular robotics to enable autonomous design, planning, and execution of biological experiments.
Abstract
Large language models (LLMs) and vision–language models (VLMs) hold transformative potential for biological research by enabling autonomous experimentation. However, their application is hindered by rigid protocol design, limited adaptability to dynamic lab conditions, inadequate error handling, and high operational complexity. We introduce BioMARS, a multi-agent system combining LLMs, VLMs, and modular robotics to autonomously design and execute biological experiments.
BioMARS Hierarchical Architecture
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Biologist Agent: Synthesizes experimental protocols via retrieval-augmented generation.
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Technician Agent: Translates protocols into executable robotic pseudo-code.
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Inspector Agent: Ensures procedural integrity through multimodal perception and anomaly detection.
The system autonomously performs cell passaging and culture tasks, achieving or exceeding manual performance in viability, consistency, and morphological integrity. It also supports context-aware optimization, outperforming conventional strategies in differentiating retinal pigment epithelial cells. A web interface enables real-time human-AI collaboration, while a modular backend allows scalable integration with laboratory hardware. These results demonstrate the feasibility of generalizable, AI-driven laboratory automation and the transformative role of language-based reasoning in biological research.
System Architecture
Agents Overview
| Agent | Function |
|---|---|
|
Biologist Agent
|
Generates experimental protocols using retrieval-augmented generation (RAG) |
|
Technician Agent
|
Converts natural language protocols into robotic pseudo-code |
|
Inspector Agent
|
Monitors processes and detects anomalies via multimodal perception |
Key Features
Autonomous Experiment Design & Execution
Multimodal Perception & Anomaly Detection
Automated Cell Culture & Passaging
Context-Aware Optimization
Modular Integration/Software Integration
Paper
For more detailed information about BioMARS, please refer to our research paper.
BioMARS: A Multi-Agent Robotic System for Autonomous Biological Experiments
Yibo Qiu, Zan Huang, Zhiyu Wang, Handi Liu, Yiling Qiao, Yifeng Hu, Shu'ang Sun, Hangke Peng, Ronald X Xu, Mingzhai Sun
Citation
title={BioMARS: A Multi-Agent Robotic System for Workflow Automation in Biological Experiments},
author={Yibo Qiu, Zan Huang, Zhiyu Wang, Handi Liu, Yiling Qiao, Yifeng Hu, Shu'ang Sun, Hangke Peng, Ronald X Xu, Mingzhai Sun},
journal={arXiv preprint arXiv:2507.01485},
year={2025}
}