WetRobo: A Reproducible Robot Kit for Coding Agents in Biological Laboratories
cs.AI, cs.RO
Submitted: 2026-09-16
Updated: 2026-09-16
Comments: 9 pages, 11 figures, 2 tables. Code and demonstrations: https://github.com/tsudalab/WetRobo
Code: https://github.com/tsudalab/WetRobo
Project page: https://opencv.org
License: http://creativecommons.org/licenses/by/4.0/
The gist: Automating biological research requires general-purpose, reproducible robot systems that allow individual wet-lab researchers to delegate robot tasks without performing teleoperation or
Terminology
Abstract
Automating biological research requires general-purpose, reproducible robot systems that allow individual wet-lab researchers to delegate robot tasks without performing teleoperation or neural-network training. Vision-language-action policies have been proposed for general-purpose arms, but can lose performance when their operating environment changes. We therefore built WetRobo, a robot kit that can readily transfer between laboratories. It consists of one robot arm, laboratory equipment (an incubator, a reagent bottle with a cap, and a Petri dish), the existing code that moves the arm, teleoperation demonstrations of each task that we recorded, and a general AGENTS.md skill file. A biological experimentalist provides natural-language tasks without collecting local teleoperation training data or training a neural network. The coding agent observes the local laboratory and writes and executes programs, using external tools as needed for adaptation. We demonstrate use of WetRobo with OpenAI Codex (gpt-5.6-sol) on three successful tasks: lifting a Petri dish lid, removing a bottle cap, and opening the incubator door, all in real-world laboratories. The coding agent achieved the cap task in both laboratories, Lab X and Lab Y, whereas a VLA fine-tuned on Lab X demonstrations succeeded there but failed to transfer to Lab Y. These results point to a practical route for laboratory robotics: instead of training a policy for each laboratory, distribute a kit and let a coding agent adapt it in each laboratory. Code, demonstrations, and the evolved programs are available at https://github.com/tsudalab/WetRobo.
Sources
- RoboCulture: A Robotics Platform for Automated Biological Experimentation
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
- LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
- BioProVLA-Agent: An Affordable, Protocol-Driven, Vision-Enhanced VLA-Enabled Embodied Multi-Agent System with Closed-Loop-Capable Reasoning for Biological Laboratory Manipulation
- ProtoAct: Turning Wet-Lab Protocols into Embodied Robotic Actions
- VLS: Steering Pretrained Robot Policies via Vision-Language Models
- CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
- ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
- SAM 3: Segment Anything with Concepts
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