RoboCulture: A Robotics Platform for Automated Biological Experimentation

2025-05-20

AI Summary

Biological experimentation automation faces challenges including millimeter-scale precision requirements, long-duration multi-step operations, and the dynamic nature of living systems—constraints under which existing platforms struggle to balance cost, flexibility, and robustness. To address this, we propose a low-cost, highly adaptive autonomous robotic platform integrating real-time optical density (OD) visual feedback, force sensing, and a modular behavior-tree-based decision framework. This enables end-to-end automation of droplet pipetting, cross-device coordination, growth-state monitoring, and closed-loop regulation. Crucially, our work introduces the first fault-tolerant autonomous execution paradigm specifically designed for long-duration, failure-sensitive biological assays, thereby bridging a critical gap in research-grade laboratory automation. In a 15-hour yeast cultivation experiment, the system operated without human intervention, achieving full autonomy while significantly improving experimental reproducibility and throughput.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Distributed Problem Solving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Automating biological experimentation remains challenging due to the need for millimeter-scale precision, long and multi-step experiments, and the dynamic nature of living systems. Current liquid handlers only partially automate workflows, requiring human intervention for plate loading, tip replacement, and calibration. Industrial solutions offer more automation but are costly and lack the flexibility needed in research settings. Meanwhile, research in autonomous robotics has yet to bridge the gap for long-duration, failure-sensitive biological experiments. We introduce RoboCulture, a cost-effective and flexible platform that uses a general-purpose robotic manipulator to automate key biological tasks. RoboCulture performs liquid handling, interacts with lab equipment, and leverages computer vision for real-time decisions using optical density-based growth monitoring. We demonstrate a fully autonomous 15-hour yeast culture experiment where RoboCulture uses vision and force feedback and a modular behavior tree framework to robustly execute, monitor, and manage experiments.

Problem

• Automating biological experiments with millimeter-scale precision
• Reducing human intervention in multi-step biological workflows
• Bridging the gap for long-duration, failure-sensitive experiments

Innovation

• General-purpose robotic manipulator automates biological tasks
• Computer vision enables real-time optical density monitoring
• Modular behavior tree framework manages experiments robustly