Transforming materials discovery plays a pivotal role in addressing global challenges. The applications of new materials could range from clean energy storage, to sustainable polymers and packaging for consumer products towards a more circular economy, to drugs and therapeutics. Stemming from the COVID-19 pandemic, where scientists had to halt experiments due to stringent social distancing measures or accelerate their efforts towards quickly producing a vaccine, there has recently been an increased interest in using robotics and automation in laboratory environments. The challenge here is that laboratories have been designed by and for humans and thus the available glassware, tools and equipment pose difficult problems for traditional automation methods that are inherently open loop and not adaptable. Learning-based methods that rely on autonomous trial and error are increasingly being used to achieve robotic tasks that could not be previously addressed with automation.

The potential use of robotics for laboratory experiments offers an attractive route to alleviate scientists from tedious tasks while accelerating the process of obtaining new materials, where topical issues such as climate change and disease risks worldwide would greatly benefit. While some experimental workflows can already benefit from automation, it is common that sample preparation is still carried out manually due to the high level of motor function required when dealing with heterogeneous systems, e.g., 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 have to 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 that is the 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. To realise that, we first create a simulation environment with a Panda Franka Emika robot using a laboratory scraper which is inserted into a simulated vial, to demonstrate how a scraping policy can be learned successfully. We then evaluate our method on a real robotic manipulator in laboratory settings, and show that our method can autonomously scrape powder across various setups.