WetRobo：用于生物实验室编码智能体的可复现机器人套件
WetRobo: A Reproducible Robot Kit for Coding Agents in Biological Laboratories
- The University of Tokyo（东京大学）
- RIKEN Pioneering Research Institute（日本理化学研究所先驱研究研究所）
- New York University（纽约大学）
- Google DeepMind（谷歌DeepMind）
- National Institute for Materials Science（日本国立材料科学研究所）
- RIKEN Center for Advanced Intelligence Project（日本理化学研究所先进智能项目中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
WetRobo提出一种可复现的机器人套件，使编码智能体在真实实验室中通过自然语言和程序执行任务，无需训练神经网络，并在跨实验室转移中优于VLA策略。
AI中文摘要：
自动化生物学研究需要通用、可复现的机器人系统，使个体湿实验室研究人员无需进行遥操作或神经网络训练即可委派机器人任务。视觉-语言-动作策略已被提出用于通用机械臂，但在其操作环境变化时可能会损失性能。因此，我们构建了WetRobo，一个可在实验室之间轻松转移的机器人套件。它由一个机械臂、实验室设备（一个培养箱、一个带盖的试剂瓶和一个培养皿）、现有的移动机械臂的代码、我们记录的每个任务的遥操作演示，以及一个通用的此http URL技能文件组成。生物实验人员提供自然语言任务，无需收集本地遥操作训练数据或训练神经网络。编码智能体观察本地实验室，编写并执行程序，在需要时使用外部工具进行适应。我们展示了使用OpenAI Codex（gpt-5.6-sol）在三个成功任务中使用WetRobo：提起培养皿盖、移除瓶盖和打开培养箱门，所有这些均在真实实验室中完成。编码智能体在两个实验室（Lab X和Lab Y）中均完成了瓶盖任务，而在Lab X演示上微调的VLA在Lab X中成功但未能转移到Lab Y。这些结果指向了实验室机器人学的一条实用路径：不是为每个实验室训练一个策略，而是分发一个套件，让编码智能体在每个实验室中适应它。代码、演示和演化程序可在该https URL获取。
英文摘要：
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.
