  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.
