Revolutionizing clean energy labs: Robotic imitation learning for efficient fabrication AI-powered electrical units assembly platform

Abstract

The energy industry, now in an era of digitization driven by computational design, is gradually moving towards automating the entire process from computational prediction to device assembly, aiming to minimize the reliance on time-consuming, manual trial-and-error validation. In this study, guided by computational density functional theory (DFT) predictions, a humanoid robotic arm, based on artificial intelligence (AI), was creatively utilized to assemble clean energy devices, solid oxide fuel cells (SOFCs). The material

(LBSF) was DFT-predicted to have high oxygen reduction reactions (ORRs) ability, suitable for the cathode in SOFCs compared to the conventional

(LSF). The material was made into ink then passed to the assembly platform with AI-driven robotics. AI-driven robotics was employed with an imitation learning method to effectively learn skills directly from human demonstrations, thereby alleviating researchers from labor-intensive tasks. We demonstrate our approach for autonomous SOFCs fabrication. For easy platform usage in the future, Large Language Models (LLMs) were incorporated to understand human commands. Visual information was captured by an RGBD camera to identify and locate the cathode painting spot. An imitation learning framework was then applied to learn the painting path from human operations and can be generalized to different conditions. The auto-fabricated single cells with the DFT-predicted LBSF cathode were tested and achieved a power density of 966 $mW/c{m}^2$ at 700 $°C$, more than double the performance of LSF. By integrating computational design with an AI-driven assembly platform, this study marks an initial step towards an AI-driven material lab, exponentially accelerating material design in the near future. The platform can also help disabled researchers achieve their ideas through the behavior cloning approach.