ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization
This addresses the problem of manual labor and inefficiency in chemistry labs for chemists, representing a novel application rather than an incremental improvement.
The paper tackles the problem of resource- and labor-intensive chemistry experiments by introducing ORGANA, a robotic assistant that automates diverse experiments, resulting in a 50% reduction in frustration and physical demand and an 80.3% time saving for users.
Chemistry experiments can be resource- and labor-intensive, often requiring manual tasks like polishing electrodes in electrochemistry. Traditional lab automation infrastructure faces challenges adapting to new experiments. To address this, we introduce ORGANA, an assistive robotic system that automates diverse chemistry experiments using decision-making and perception tools. It makes decisions with chemists in the loop to control robots and lab devices. ORGANA interacts with chemists using Large Language Models (LLMs) to derive experiment goals, handle disambiguation, and provide experiment logs. ORGANA plans and executes complex tasks with visual feedback, while supporting scheduling and parallel task execution. We demonstrate ORGANA's capabilities in solubility, pH measurement, recrystallization, and electrochemistry experiments. In electrochemistry, it executes a 19-step plan in parallel to characterize quinone derivatives for flow batteries. Our user study shows ORGANA reduces frustration and physical demand by over 50%, with users saving an average of 80.3% of their time when using it.