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Introduction to Robotic Laboratory Automation

Robotic Laboratory Automation for Materials Discovery

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Series Overview

Experimental automation in materials science is an essential technology for accelerating research and development. In this series, you will learn practical techniques ranging from autonomous experimentation using robotic arms and automated liquid-handling systems, to closed-loop optimization combined with Bayesian optimization, and remote experimentation via cloud labs.

Tasks that took anywhere from several hours to a full day per material with conventional manual experiments are shortened to minutes or tens of minutes through automation, and 24-hour operation dramatically improves productivity. Through leading examples such as Berkeley A-Lab, RoboRXN, and Emerald Cloud Lab, we explore the future of materials research.

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Learning Objectives

By studying this series, you will acquire the following skills and knowledge:

  1. Fundamentals of Experimental Automation: Understanding robotic arm control, liquid/solid handling, and sensor integration
  2. Closed-Loop Optimization: Integrating Bayesian optimization with robotic experiments for autonomous materials exploration
  3. Robotics Programming: Practical use of PyLabRobot and the OpenTrons Python API
  4. Cloud Lab Utilization: How to use remote experimentation platforms such as Emerald Cloud Lab
  5. Real-World Applications: Application to catalyst screening, quantum dot synthesis, and battery materials exploration

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Target Audience

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Prerequisites

Required Knowledge

Recommended Knowledge

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Series Structure

Chapter 1: The Need for and Current State of Materials Experiment Automation

Learning Time: 20-25 min | Code Examples: 3-4 Learning Goals:

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Chapter 2: Fundamentals of Robotic Experimentation

Learning Time: 25-30 min | Code Examples: 5-6 Learning Goals:

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Chapter 3: Closed-Loop Optimization

Learning Time: 25-30 min | Code Examples: 6-7 Learning Goals:

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Chapter 4: Cloud Labs and Remote Experimentation

Learning Time: 20-25 min | Code Examples: 4-5 Learning Goals:

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Chapter 5: Real-World Applications and Careers

Learning Time: 15-20 min | Code Examples: 2-3 Learning Goals:

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How to Proceed with Your Studies

Recommended Learning Flow

flowchart LR A[Chapter 1\nNeed for Automation] --> B[Chapter 2\nRobotics Fundamentals] B --> C[Chapter 3\nClosed-Loop] C --> D[Chapter 4\nCloud Labs] D --> E[Chapter 5\nReal-World Applications] style A fill:#e1f5ff style B fill:#fff4e1 style C fill:#ffe1e1 style D fill:#f0e1ff style E fill:#e1ffe1

Estimated Learning Time

Practical Tips

  1. Start with simulation: If you don't have physical hardware, learn in a virtual environment such as PyBullet
  2. Start small: Begin with simple liquid handling and gradually increase complexity
  3. Leverage existing platforms: Use open platforms such as the OpenTrons OT-2
  4. Consider cloud labs: If you want to keep initial investment low, use services such as Emerald Cloud Lab

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Tools and Libraries Used

Main Libraries

Virtual Environments and Simulation

Cloud Platforms

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Environment Setup

Setting Up the Python Environment

Create a virtual environment (recommended)


python -m venv robotic-lab-env


source robotic-lab-env/bin/activate  # macOS/Linux


robotic-lab-env\Scripts\activate  # Windows



Install required libraries


pip install pylablib opentrons scikit-optimize botorch pandas requests matplotlib seaborn jupyter



For simulation (optional)


pip install pybullet gym

Setting Up the OpenTrons OT-2 (when using physical hardware)

Download the OpenTrons App (GUI)


https://opentrons.com/ot-app/



Install the Python API


pip install opentrons



Launch the emulator (learn without physical hardware)


opentrons_simulate protocol.py

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Related Series

This series is related to the following content:

Prerequisite Series (recommended)

Advanced Series

Recommended Concurrent Study

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Learning Resources

Official Documentation

Papers and Books

Online Courses

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FAQ (Frequently Asked Questions)

Q1: Can I learn without physical robot hardware?

A: Yes, you can. You can learn using simulation environments such as the OpenTrons Emulator and PyBullet. In addition, using a cloud lab lets you run experiments without owning any hardware.

Q2: How much initial investment is required?

A: The OpenTrons OT-2 starts at around $10,000. With a cloud lab (Emerald Cloud Lab), you can pay per use with zero initial investment. At universities and research institutions, you can also start by retrofitting existing equipment for automation.

Q3: Is it okay if I have little programming experience?

A: Python basics (variables, functions, loops) are sufficient. The OpenTrons API is very intuitive, and you'll be able to write basic protocols within a few hours.

Q4: Can I apply this to my own research field?

A: If your experiments involve liquid samples (synthesis, evaluation, analysis), application is highly likely. For solid samples, additional equipment such as powder dispensers is needed, but it is technically feasible.

Q5: Is safety assured?

A: This series emphasizes safe design and error handling. You will always implement emergency-stop functions, anomaly detection, and fail-safe design.

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Community and Support

Discussion

Contributing

This content is open source. We welcome GitHub Pull Requests for pointing out errors, suggesting improvements, adding code examples, and more.

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License

This content is provided under the CC BY 4.0 (Creative Commons Attribution 4.0 International) license.

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Revision History

| Version | Date | Update Details |

|----------|------|---------|

| 1.0 | 2025-10-17 | Initial release (all 5 chapters) |

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Next Steps

Once you're ready, start learning from Chapter 1: The Need for and Current State of Materials Experiment Automation!

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Author: Yusuke Hashimoto (Tohoku University) Created: October 17, 2025 Updated: October 17, 2025
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