RoboCulture: A Robotics Platform for Automated Biological Experimentation
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915303112310784 |
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| author | Angers, Kevin Darvish, Kourosh Yoshikawa, Naruki Okhovatian, Sargol Bannerman, Dawn Yakavets, Ilya Shkurti, Florian Aspuru-Guzik, Alán Radisic, Milica |
| author_facet | Angers, Kevin Darvish, Kourosh Yoshikawa, Naruki Okhovatian, Sargol Bannerman, Dawn Yakavets, Ilya Shkurti, Florian Aspuru-Guzik, Alán Radisic, Milica |
| contents | 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. Video demonstrations of RoboCulture can be found at https://ac-rad.github.io/roboculture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RoboCulture: A Robotics Platform for Automated Biological Experimentation Angers, Kevin Darvish, Kourosh Yoshikawa, Naruki Okhovatian, Sargol Bannerman, Dawn Yakavets, Ilya Shkurti, Florian Aspuru-Guzik, Alán Radisic, Milica Robotics 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. Video demonstrations of RoboCulture can be found at https://ac-rad.github.io/roboculture. |
| title | RoboCulture: A Robotics Platform for Automated Biological Experimentation |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.14941 |