文献类型：期刊文献

英文题名：An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics

作者：Liu, Zhe[1,2]; Jin, Huanbo[1,2]; Du, Zhaohui[1,2]; Wang, Zhe[1,2]; Xu, He[2]; Li, Peijia[2]; Gu, Jiaming[1,2]; Lu, Quan[1,2]; Wang, Qi[3]; Ji, Bin[1,2]; Xiao, Ting[1,2]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; [2] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China; [3] Department of Laboratory Medicine, Ruijin Hospital, Shanghai Jiao Tong, University School of Medicine, Shanghai, China

年份：2026

外文期刊名：arXiv

收录：EI(收录号：20260342907)

语种：英文

外文关键词：Computer simulation languages - Laboratories - Natural language processing systems - Personnel training - Pipelines - Robot learning - Robots - Simulation platform

摘要：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 π 0 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. Copyright ? 2026, The Authors. All rights reserved.