# RoboCulture: A Robotics Platform for Automated Biological Experimentation

arXiv: https://arxiv.org/abs/2505.14941 · HTML: https://mlsanity.com/paper/2505.14941/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2025.

## Built on by (most cited first, 7 of 7)

- [Keyframe-Guided Structured Rewards for Reinforcement Learning in Long-Horizon Laboratory Robotics](https://mlsanity.com/paper/2603.00719/) (2026)
- [A Framework for Closed-Loop Robotic Assembly, Alignment and Self-Recovery of Precision Optical Systems](https://mlsanity.com/paper/2603.21496/) (2026)
- [GAMORA: A Gesture Articulated Meta Operative Robotic Arm for Hazardous Material Handling in Containment-Level Environments](https://mlsanity.com/paper/2506.14513/) (2025)
- [Data-Centric Visual Development for Self-Driving Labs](https://mlsanity.com/paper/2512.02018/) (2025)
- [A Vision for Multisensory Intelligence: Sensing, Science, and Synergy](https://mlsanity.com/paper/2601.04563/) (2026)
- [GRAFT: Grounded and Efficient Online Reinforcement Adaptation for Fine-Grained Robot Manipulation](https://mlsanity.com/paper/2608.27079/) (2026)
- [WetRobo: A Reproducible Robot Kit for Coding Agents in Biological Laboratories](https://mlsanity.com/paper/2609.18435/) (2026)

## Abstract (arXiv)

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.