Embodied intelligence in manufacturing: leveraging large language models for autonomous industrial robotics

Haolin Fan (), Xuan Liu (), Jerry Ying Hsi Fuh (), Wen Feng Lu () and Bingbing Li ()
Additional contact information
Haolin Fan: National University of Singapore
Xuan Liu: National University of Singapore
Jerry Ying Hsi Fuh: National University of Singapore
Wen Feng Lu: National University of Singapore
Bingbing Li: National University of Singapore

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 2, No 19, 1157 pages

Abstract: Abstract This paper delves into the potential of Large Language Model (LLM) agents for industrial robotics, with an emphasis on autonomous design, decision-making, and task execution within manufacturing contexts. We propose a comprehensive framework that includes three core components: (1) matches manufacturing tasks with process parameters, emphasizing the challenges in LLM agents’ understanding of human-imposed constraints; (2) autonomously designs tool paths, highlighting the LLM agents’ proficiency in planar tasks and challenges in 3D spatial tasks; and (3) integrates embodied intelligence within industrial robotics simulations, showcasing the adaptability of LLM agents like GPT-4. Our experimental results underscore the distinctive performance of the GPT-4 agent, especially in Component 3, where it is outstanding in task planning and achieved a success rate of 81.88% across 10 samples in task completion. In conclusion, our study accentuates the transformative potential of LLM agents in industrial robotics and suggests specific avenues, such as visual semantic control and real-time feedback loops, for their enhancement.

Keywords: Large language models (LLMs) agents; Industrial robotics; Autonomous design; Decision-making; Embodied intelligence (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02294-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02294-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02294-y

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().