BioMARS: A Multi-Agent Robotic System for Autonomous Biological Experiments

• URL: http://arxiv.org/abs/2507.01485v1
• Date: Wed, 02 Jul 2025 08:47:02 GMT
• Title: BioMARS: A Multi-Agent Robotic System for Autonomous Biological Experiments
• Authors: Yibo Qiu, Zan Huang, Zhiyu Wang, Handi Liu, Yiling Qiao, Yifeng Hu, Shu'ang Sun, Hangke Peng, Ronald X Xu, Mingzhai Sun,
• Abstract summary: 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.
• Score: 8.317138109309967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) and vision-language models (VLMs) have the potential to transform biological research by enabling autonomous experimentation. Yet, their application remains constrained by rigid protocol design, limited adaptability to dynamic lab conditions, inadequate error handling, and high operational complexity. Here we introduce BioMARS (Biological Multi-Agent Robotic System), an intelligent platform that integrates LLMs, VLMs, and modular robotics to autonomously design, plan, and execute biological experiments. BioMARS uses a hierarchical architecture: the Biologist Agent synthesizes protocols via retrieval-augmented generation; the Technician Agent translates them into executable robotic pseudo-code; and the Inspector Agent ensures procedural integrity through multimodal perception and anomaly detection. The system autonomously conducts cell passaging and culture tasks, matching 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 highlight the feasibility of generalizable, AI-driven laboratory automation and the transformative role of language-based reasoning in biological research.

Related papers

• An AI-native experimental laboratory for autonomous biomolecular engineering [12.382004681010915]
  We present an AI-native autonomous laboratory, targeting highly complex scientific experiments for applications like autonomous biomolecular engineering.<n>This system autonomously manages instrumentation, formulates experiment-specific procedures and optimizations, and concurrently serves multiple user requests.<n>It also enables applications in fields such as disease diagnostics, drug development, and information storage.
  arXiv  Detail & Related papers  (2025-07-03T07:21:19Z)
• Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI [98.19195693735487]
  We propose the paradigm of Intelligent Science Laboratories (ISLs)<n>ISLs are a multi-layered, closed-loop framework that deeply integrates cognitive and embodied intelligence.<n>We argue that such systems are essential for overcoming the current limitations of scientific discovery.
  arXiv  Detail & Related papers  (2025-06-24T13:31:44Z)
• Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data [33.7054351451505]
  We introduce Agentomics-ML, a fully autonomous agent-based system designed to produce a classification model.<n>We show that Agentomics-ML outperforms existing state-of-the-art agent-based methods in both generalization and success rates.
  arXiv  Detail & Related papers  (2025-06-05T19:44:38Z)
• LabUtopia: High-Fidelity Simulation and Hierarchical Benchmark for Scientific Embodied Agents [103.65422553044816]
  LabUtopia is a comprehensive simulation and benchmarking suite designed to facilitate the development of generalizable, reasoning-capable embodied agents.<n>It supports 30 distinct tasks and includes more than 200 scene and instrument assets.<n>We demonstrate that LabUtopia offers a powerful platform for advancing the integration of perception, planning, and control in scientific-purpose agents.
  arXiv  Detail & Related papers  (2025-05-28T17:50:53Z)
• Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs [0.0]
  Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation.<n>But they face challenges in orchestrating complex, integrating diverse instruments and AI models, and managing data efficiently.<n>Artificial addresses these issues with a comprehensive orchestration and scheduling system.<n>It unifies lab operations, automates, and integrates AI-driven decision-making.
  arXiv  Detail & Related papers  (2025-04-01T17:22:50Z)
• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments [112.25067497985447]
  We introduce BioDiscoveryAgent, an agent that designs new experiments, reasons about their outcomes, and efficiently navigates the hypothesis space to reach desired solutions.<n>BioDiscoveryAgent can uniquely design new experiments without the need to train a machine learning model.<n>It achieves an average of 21% improvement in predicting relevant genetic perturbations across six datasets.
  arXiv  Detail & Related papers  (2024-05-27T19:57:17Z)
• Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models [4.762323642506732]
  Genome-scale metabolic network models (GEMs) are widely used to evaluate genetic engineering of biological systems.<n>GEMs often fail to accurately predict the behaviour of genetically engineered cells, primarily due to incomplete annotations of gene interactions.<n>We develop a new system, $BMLP_active$, which guides cost-effective experimentation and uses interpretable logic programs to encode a state-of-the-art GEM of a model bacterial organism.
  arXiv  Detail & Related papers  (2024-05-10T09:51:06Z)
• CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments [51.41735920759667]
  Large Language Models (LLMs) have shown promise in various tasks, but they often lack specific knowledge and struggle to accurately solve biological design problems. In this work, we introduce CRISPR-GPT, an LLM agent augmented with domain knowledge and external tools to automate and enhance the design process of CRISPR-based gene-editing experiments.
  arXiv  Detail & Related papers  (2024-04-27T22:59:17Z)
• Empowering Biomedical Discovery with AI Agents [15.125735219811268]
  We envision "AI scientists" as systems capable of skeptical learning and reasoning. Biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
  arXiv  Detail & Related papers  (2024-04-03T16:08:01Z)
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [55.30328162764292]
  Chemist-X is a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis.<n>The agent uses retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions.<n>Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories.
  arXiv  Detail & Related papers  (2023-11-16T01:21:33Z)
• When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development [3.687740185234604]
  Machine learning (ML) has significantly contributed to the development of bioprocess engineering, but its application is still limited. This review provides a comprehensive overview of ML-based automation in bioprocess development.
  arXiv  Detail & Related papers  (2022-09-02T14:30:49Z)
• Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents [61.36681529571202]
  We describe a new concept for reproducible robotics research that integrates development and benchmarking. One of the central components of this setup is the Duckietown Autolab, a standardized setup that is itself relatively low-cost and reproducible. We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs.
  arXiv  Detail & Related papers  (2020-09-09T15:31:29Z)

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.