RoboCulture cleverly attaches new pipette tips using a spiral motion and force feedback.
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.
RoboCulture performing cell expansion (30x speed). After completing the optical density monitoring procedure, the robot initiates the well-splitting process. It first retrieves the pipette from its stand and attaches a fresh pipette tip. Growth media is then aspirated and dispensed into 18 empty wells. The robot proceeds by resuspending the first saturated well and distributing its contents into three of the pre-filled wells, followed by the disposal of the used pipette tip. For each of the remaining five saturated wells, a new pipette tip is attached, used, and discarded in sequence.
RoboCulture cleverly attaches new pipette tips using a spiral motion and force feedback.
The contaminated pipette is removed using a 3D-printed pipette tip remover.
Using a camera mounted on the robot, RoboCulture perceives the positions of the wells and the pipette tip. This video shows the perception output while moving the robot arm manually.
Even under disturbances, RoboCulture can accurately detect the position of the wells and reposition the pipette tip accordingly.
RoboCulture also uses its camera to monitor the growth of the yeast culture and make experiment decisions based on the optical density of the wells. The robot pans across the well plate to capture images of the wells at regular intervals.
By imaging the wells at multiple intervals, RoboCulture constructs a growth curve for each well, informing its decision to split the wells.
@article{angers2025roboculture,
title={RoboCulture: A Robotics Platform for Automated Biological Experimentation},
author={Kevin Angers and Kourosh Darvish and Naruki Yoshikawa and Sargol Okhovatian and Dawn Bannerman and Ilya Yakavets and Florian Shkurti and Alán Aspuru-Guzik and Milica Radisic},
year={2025},
eprint={2505.14941},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2505.14941},
}