发表机构
Data Science and Learning Division, Argonne National Laboratory; Department of Computer Science, University of Chicago（阿贡国家实验室数据科学与学习部; 芝加哥大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究开源液体处理机器人运行问题，提出AEGIS两层防护系统。一层结合检测规则库与大语言模型验证协议，二层用主成分分析模型监测运行轨迹，经实验验证有效，统一了检测感知验证与视觉监测，且开源。
AI 中文摘要
自动驾驶实验室越来越依赖低成本液体处理设备，如Opentrons OT-2，其缺乏基于压力的抽吸监测且通常开环运行，存在两种未被检测到的故障模式。我们提出AEGIS，它是一个两层防护系统。第一层将精心策划的机器可读检测规则数据库与对OT-2 Python代码进行推理的大语言模型配对，在跨五个检测家族的24个协议基准测试中调整后的F1达到0.97；第二层将主成分分析世界模型拟合到YOLO裁剪的四帧移液器轨迹上。通过现场演示验证了其有效性，且开源的AEGIS统一了飞行前检测感知验证与运行时视觉监测。
英文摘要
Self-driving laboratories increasingly rely on low-cost liquid handlers such as the Opentrons OT-2, which ship without the pressure-based aspiration monitoring of Hamilton or Tecan systems and are typically run open-loop. Two failure modes go undetected: protocols that are syntactically valid but violate assay-specific invariants (e.g., tip reuse between a PCR template and a no-template control), and physical execution failures (partial dispense, air bubbles, missing tips) at runtime. We present AEGIS, a two-layer guardian for both. Layer 1 pairs a curated machine-readable assay rule database with an LLM that reasons over OT-2 Python code, reaching an adjusted F1 of 0.97 on a 24-protocol benchmark across five assay families and beating rules-only and LLM-only ablations across five backends; a free open-weight model ties the best proprietary one, so no paid API is required. Layer 2 fits a PCA world model to YOLO-cropped four-frame pipette trajectories; under a leakage-free leave-one-plate-out evaluation it reaches average precision 0.89 and operating-point F1 0.71 (AUROC 0.80), a deployment-faithful number that matches the live demonstration, and we characterize the small-pipette (p20) resolution limit (F1 0.47). A live demonstration on a physical OT-2 (five replicates per condition) catches planted no-tip failures deterministically and partial dispense on coloured dyes, with an always-VLM self-vote gate lifting partial-dispense recall to 5/5; transparent water is a principled limit of any front-view-only monitor, which AEGIS surfaces as low-confidence VLM reasoning rather than a wrong verdict. Cascade triage holds VLM cost near $1.63 per plate versus $10.33 for an always-VLM baseline. AEGIS is open source and, to our knowledge, the first system to unify pre-flight assay-aware validation with runtime visual monitoring for an open-source liquid handler.
