Wet-lab robots can improve the reproducibility, throughput, and safety of biomedical experiments, but scaling their learning requires customizable simulators for safe and reproducible task generation, open editable laboratory assets, and efficient pipelines that turn limited demonstrations into usable training data. We present Pipette, an embodied simulation platform, benchmark, and data-efficient augmentation framework for wet-lab robot learning. Pipette releases over 43 open-source and re-editable wet-lab assets, together with an extensible asset-building pipeline. A key component of Pipette is its simulation-based data augmentation pipeline, replaying human demonstrations in simulation, applies lighting, camera, speed, and action perturbations, and filters generated episodes with automatic task success checks, rapidly expanding usable training data from limited manual demonstrations. We further introduce an 11-task wet-lab embodied benchmark covering sample handling, culture-ware manipulation, device operation, and precision placement. With only 30 demonstrations per task, ACT achieves 65.5% average success rate, while simulation augmentation improves SmolVLA from 44.1% to 74.7% and π0 from 40.4% to 46.5%, validating the effectiveness of Pipette for data-efficient VLA training and evaluation. Pipette also supports natural-language-driven scene construction and task registration, lowering the barrier for non-expert users to define new wet-lab robotic tasks.

翻译：湿实验室机器人能够提升生物医学实验的可重复性、通量和安全性，但扩展其学习能力需要可定制的仿真器以支持安全且可重复的任务生成、开放可编辑的实验室资产，以及将有限示范转化为可用训练数据的高效流水线。我们提出Pipette——面向湿实验室机器人学习的具身仿真平台、基准测试与数据高效增强框架。Pipette开源发布了超过43种可重新编辑的湿实验室资产，并配套可扩展的资产构建流水线。其核心组件是基于仿真的数据增强流水线：在仿真中回放人类示范，施加光照、相机、速度与动作扰动，并通过自动任务成功判定过滤生成的情节，从而从有限人工示范中快速扩展可用训练数据。我们进一步引入包含11个任务的湿实验室具身基准测试，涵盖样本处理、培养器皿操作、设备操控与精准放置。在每项任务仅使用30个示范的情况下，ACT实现了65.5%的平均成功率，而仿真增强将SmolVLA从44.1%提升至74.7%，将π0从40.4%提升至46.5%，验证了Pipette在数据高效的VLA训练与评估中的有效性。Pipette还支持自然语言驱动的场景构建与任务注册，降低了非专业用户定义新湿实验室机器人任务的门槛。