🤖 AI Summary
This work addresses the challenges researchers face in coordinating hardware control, data analysis, and experimental planning when building autonomous experimentation systems. To overcome these barriers, the authors propose a general-purpose, service-oriented autonomous experimentation platform featuring a language-agnostic, modular architecture. The platform enables users to define custom modules for hardware control, data processing, and experimental planning, with efficient inter-module communication facilitated through protobuf and gRPC. Integrated components—including a unified central control interface, automated UI generation, data management infrastructure, and experimental design tools—support closed-loop autonomous experimentation. By significantly lowering deployment complexity while enhancing flexibility and scalability, this approach allows researchers to concentrate on domain-specific scientific innovation rather than system integration overhead.
📝 Abstract
ARES OS 2.0 (hereinafter ARES OS) is an open-source software suite to enable laboratory automation and closed-loop autonomous experimentation. Its function is to orchestrate experimental actions and data handoff between lab equipment, analysis routines, and experimental planning modules through a service-oriented architecture. ARES OS is abstracted to apply to general experimental flows common in materials science, chemistry, and biology and related disciplines. The core of ARES OS provides central control over all modules, along with the heavy lifting of UI creation, data management, and experimental design tools. ARES OS modules communicate with the core software over protobuf and gRPC, allowing them to be language-agnostic and user-creatable. This allows users to easily implement modules that control experimental hardware, process collected data , or plan experiments to meet their specific research needs. ARES OS lowers the barrier to entry for researchers to build their own self-driving labs, allowing them to focus on scientific programming for their use case and reducing the effort and time needed to bring an autonomous experimentation system online.