Pipette让实验室机器人用仿真和少量示范高效学习，提升数据利用效率。
Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
- 基于仿真生成多样化的实验操作数据，支持多机械臂统一接口。
- 30次示范下模型成功率从40.4%提升至71.8%，显著增强训练效果。
- 支持自然语言定义任务，降低非专家使用门槛。
湿实验机器人可提升生物医学实验的可重复性、通量与安全性，但其规模化学习需定制化模拟器、开放可编辑的实验室资产及高效的数据转化流程。我们提出Pipette，一个面向湿实验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通过可扩展的资产构建管道，提供超过100个开源且可重编辑的湿实验资产，内置腾讯混元支持，实现文本与图像驱动的3D资产生成；并通过统一仿真接口支持三种机械臂实体，用于任务构建、数据采集、增强与评估。其核心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检测过滤生成片段，从而从有限人工示范快速扩展可用训练数据。我们进一步引入涵盖样本处理、培养皿操作、设备控制与精确定位的12项任务湿实验具身基准。仅30次示范/任务下，ACT平均成功率60.3%；仿真增强使SmolVLA成功率从40.4%提升至71.8%，pi0从37.3%提升至44.1%，验证了Pipette在数据高效视觉-语言-动作训练中的有效性。Pipette还支持自然语言驱动场景构建与任务注册，降低非专家用户定义新任务的门槛。
原文摘要 · Abstract (English)
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 provides over 100 open-source and re-editable wet-lab assets through an extensible asset-building pipeline with built-in Tencent Hunyuan support for text- and image-conditioned 3D asset generation, and supports three robotic-arm embodiments through a unified simulation interface for task construction, data collection, augmentation, and evaluation. A key component of Pipette is its simulation-based data augmentation pipeline, which replays 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 a 12-task wet-lab embodied benchmark covering sample handling, culture-ware manipulation, device operation, and precision placement. With only 30 demonstrations per task, ACT achieves 60.3% average success rate, while simulation augmentation improves SmolVLA from 40.4% to 71.8% and pi0 from 37.3% to 44.1%, 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
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