Integrates LLMs as closed-loop tuning experts for manufacturing robots to achieve 0% failure in complex 3D printing tasks.
March 24, 2026
Original Paper
Programming Manufacturing Robots with Imperfect AI: LLMs as Tuning Experts for FDM Print Configuration Selection
arXiv · 2603.22118
The Takeaway
Instead of using LLMs as end-to-end oracles, it uses them to interpret diagnostic feedback within a Bayesian optimization loop. This framework allows 'imperfect' AI to guide robots in high-precision manufacturing environments where single errors are unacceptable.
From the abstract
We use fused deposition modeling (FDM) 3D printing as a case study of how manufacturing robots can use imperfect AI to acquire process expertise. In FDM, print configuration strongly affects output quality. Yet, novice users typically rely on default configurations, trial-and-error, or recommendations from generic AI models (e.g., ChatGPT). These strategies can produce complete prints, but they do not reliably meet specific objectives. Experts iteratively tune print configurations using evidence