Automating experimental protocol design and execution remains as a fundamental bottleneck in realizing self-driving laboratories. We introduce PRISM (Protocol Refinement through Intelligent Simulation Modeling), a framework that automates the design, validation, and execution of experimental protocols on a laboratory platform composed of off-the-shelf robotic instruments. PRISM uses a set of language-model-based agents that work together to generate and refine experimental steps. The process begins with automatically gathering relevant procedures from web-based sources describing experimental workflows. These are converted into structured experimental steps (e.g., liquid handling steps, deck layout and other related operations) through a planning, critique, and validation loop. The finalized steps are translated into the Argonne MADSci protocol format, which provides a unified interface for coordinating multiple robotic instruments (Opentrons OT-2 liquid handler, PF400 arm, Azenta plate sealer and peeler) without requiring human intervention between steps. To evaluate protocol-generation performance, we benchmarked both single reasoning models and multi-agent workflow across constrained and open-ended prompting paradigms. The resulting protocols were validated in a digital-twin environment built in NVIDIA Omniverse to detect physical or sequencing errors before execution. Using Luna qPCR amplification and Cell Painting as case studies, we demonstrate PRISM as a practical end-to-end workflow that bridges language-based protocol generation, simulation-based validation, and automated robotic execution.

翻译：实现自主实验室的一个基本瓶颈在于实验方案设计与执行的自动化。本文介绍PRISM（通过智能仿真建模实现实验方案优化）框架，该框架可在由商用机器人仪器组成的实验平台上实现实验方案的自动化设计、验证与执行。PRISM采用一组基于语言模型的智能体协同工作以生成并优化实验步骤。该流程首先从描述实验流程的网络资源中自动收集相关操作程序，随后通过规划、评估与验证的循环将其转化为结构化实验步骤（如液体处理步骤、平台布局及其他相关操作）。最终确定的步骤将被转换为阿贡国家实验室MADSci方案格式，该格式为协调多台机器人仪器（Opentrons OT-2液体处理系统、PF400机械臂、Azenta板封膜与揭膜仪）提供了统一接口，无需在步骤间进行人工干预。为评估方案生成性能，我们在受限提示与开放提示两种范式下，对单推理模型与多智能体工作流进行了基准测试。生成的方案在NVIDIA Omniverse构建的数字孪生环境中进行验证，以在执行前检测物理或顺序错误。通过Luna qPCR扩增与细胞染色作为案例研究，我们证明PRISM是一个实用的端到端工作流，实现了基于语言的方案生成、基于仿真的验证与自动化机器人执行之间的衔接。