The use of laboratory robotics for autonomous experiments offers an attractive route to alleviate scientists from tedious tasks while accelerating material discovery for topical issues such as climate change and pharmaceuticals. While some experimental workflows can already benefit from automation, sample preparation is still carried out manually due to the high level of motor function and dexterity required when dealing with different tools, chemicals, and glassware. A fundamental workflow in chemical fields is crystallisation, where one application is polymorph screening, i.e., obtaining a three dimensional molecular structure from a crystal. For this process, it is of utmost importance to recover as much of the sample as possible since synthesising molecules is both costly in time and money. To this aim, chemists scrape vials to retrieve sample contents prior to imaging plate transfer. Automating this process is challenging as it goes beyond robotic insertion tasks due to a fundamental requirement of having to execute fine-granular movements within a constrained environment (sample vial). Motivated by how human chemists carry out this process of scraping powder from vials, our work proposes a model-free reinforcement learning method for learning a scraping policy, leading to a fully autonomous sample scraping procedure. We first create a scenario-specific simulation environment with a Panda Franka Emika robot using a laboratory scraper that is inserted into a simulated vial, to demonstrate how a scraping policy can be learned successfully in simulation. We then train and evaluate our method on a real robotic manipulator in laboratory settings, and show that our method can autonomously scrape powder across various setups.

翻译：实验室机器人自主实验的运用为减轻科学家繁琐工作负担、加速气候变化及药物研发等热点领域的材料发现提供了有效途径。尽管部分实验流程已实现自动化，但样本制备仍依赖人工操作——这源于处理不同工具、化学品和玻璃器皿时所需的精细运动控制与灵巧操作能力。化学领域的基础流程之一是结晶操作，其中多晶型筛选（即从晶体中获取三维分子结构）作为典型应用，要求尽可能回收样本——因为分子合成既耗时又昂贵。为此，化学家需在转移至成像板前刮取试管内壁回收样本内容物。该过程的自动化面临特殊挑战：由于必须在受限空间（样本试管）内执行微米级精细动作，这已超越传统机器人插拔操作的范畴。受人类化学家刮取粉末操作方式的启发，本文提出一种无模型强化学习方法以学习刮取策略，实现样本刮取全流程自主化。我们首先构建了包含Panda Franka Emika机器人的场景化仿真环境，通过将实验室刮刀插入虚拟试管，验证了刮取策略在仿真中的可学习性。随后在真实实验环境中对该方法进行训练与评估，结果表明该方法能在多种实验配置下自主完成粉末刮取操作。