ARMS: an Automated Robotized human-Mimicking Solid sampling system
Year: 2026
Type: preprint
Abstract: Chemists sample solid materials such as powders, highly viscous fluids, and semi-solids with a spatula in labs every day. Yet, this action remains the key bottleneck in the development of fully automated chemistry laboratories. While a handful of specialist tools have been developed to autonomously manipulate solids, these approaches target a limited range of material properties. In this work, we approach the problem from the other perspective by studying the capabilities of chemists to gain insights into the design of a generalist solid sampling approach with the aim of spanning the entire range of possible material properties. We observed and demonstrated that the key capabilities are characterizing, identifying the material descriptors that map material properties to actions; scooping, adding material to the spatula; depositing, removing material from the spatula; weighing, measuring the mass transferred; and auxiliary actions such as cleaning the spatula. We demonstrate how each of these actions can be combined in a bimanual robot platform and sample seven of nine materials chosen to span the range of possible materials. In order to achieve this performance, we leverage low-data learning from demonstration techniques such that demonstration collection takes less than an hour and training lasts less than a minute, exploit geometric structure, and transfer to novel materials within a descriptor class. Notably, we were able to move not only simpler granular free-flowing materials such as NaCl but also sticky materials such as honey, reaching standard deviations of 1.5–3.3 mg and 2.8–10.1 mg with scoop depth predicting delivered mass at 0.70 < R2 < 0.93 and 0.28 < R2 < 0.66, respectively.
Source: ChemRxiv
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214039958