用AI视觉纠错，让机器人精准搬运易碎透明基底。
Closed-Loop Robotic Manipulation of Transparent Substrates for Self-Driving Laboratories using Deep Learning Micro-Error Correction
- 结合机器人与深度学习视觉，实时检测并修正基底搬运误差。
- 130次独立实验中首次放置准确率达98.5%，仅2次错误被自动纠正。
- 适合自动化实验室、材料发现平台及精密操作场景使用。
自驱动实验室（SDLs）已显著提升化学与材料发现的通量和自动化水平。尽管其已实现多数实验步骤的自动化，但基底的抓取与重装环节常被忽视。本文提出一种闭环式自动基底处理与更换方法（ASHE），结合机器人、双执行器分配器与深度学习驱动的计算机视觉，用于检测并纠正脆弱透明基底在搬运中的误差。通过ASHE，在130次独立重装玻璃基底的实验中，首次放置准确率达到98.5%，仅发生2次误置，均被成功识别并自动修正。该方法提升了各类基底处理的精度与可靠性，推动自驱动实验室自动化能力进一步发展，加速新型化学与材料的发现进程。
原文摘要 · Abstract (English)
Self-driving laboratories (SDLs) have accelerated the throughput and automation capabilities for discovering and improving chemistries and materials. Although these SDLs have automated many of the steps required to conduct chemical and materials experiments, a commonly overlooked step in the automation pipeline is the handling and reloading of substrates used to transfer or deposit materials onto for downstream characterization. Here, we develop a closed-loop method of Automated Substrate Handling and Exchange (ASHE) using robotics, dual-actuated dispensers, and deep learning-driven computer vision to detect and correct errors in the manipulation of fragile and transparent substrates for SDLs. Using ASHE, we demonstrate a 98.5% first-time placement accuracy across 130 independent trials of reloading transparent glass substrates into an SDL, where only two substrate misplacements occurred and were successfully detected as errors and automatically corrected. Through the development of more accurate and reliable methods for handling various types of substrates, we move toward an improvement in the automation capabilities of self-driving laboratories, furthering the acceleration of novel chemical and materials discoveries.
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