Revolutionizing clean energy labs: robotic imitation learning for efficient fabrication AI-powered electrical units assembly platform
File(s)
Published version
Author(s)
Xu, Xi
Gu, Yijun
Zhang, Tianyi
Yu, Jiwen
Skinner, Stephen
Type
Journal Article
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
La0.35Bi0.15Sr0.5FeO3-𝛿
(LBSF) was DFT-predicted to have high oxygen reduction reactions (ORRs) ability,
suitable for the cathode in SOFCs compared to the conventional La0.5Sr0.5FeO3-𝛿
(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 ∕𝑐𝑚2 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.
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
La0.35Bi0.15Sr0.5FeO3-𝛿
(LBSF) was DFT-predicted to have high oxygen reduction reactions (ORRs) ability,
suitable for the cathode in SOFCs compared to the conventional La0.5Sr0.5FeO3-𝛿
(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 ∕𝑐𝑚2 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.
Date Issued
2025-09-01
Date Acceptance
2025-04-20
Citation
Energy and AI, 2025, 21
ISSN
2666-5468
Publisher
Elsevier
Journal / Book Title
Energy and AI
Volume
21
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.egyai.2025.100517
Publication Status
Published
Article Number
100517
Date Publish Online
2025-05-01