Toward Intelligent Microfluidics: An LLM-Based Droplets Controlling Framework
Fangdi Li, Zelin Wang, Dianhua Zhang, Yuke Pan, Jianguang Zhou
Abstract
Digital microfluidics (DMF) is a versatile platform that enables parallel droplet operations and on-site programmable control, where droplet routing is a critical challenge for achieving automation. To enable automatic path generation from source to target electrodes and process-level feedback correction, this study proposes an adaptive intelligent control framework based on large language models (LLMs). The framework integrates language-guided topological map reconstruction, LLM-based path planning, and a dual closed-loop feedback mechanism to achieve high-precision droplet manipulation. To alleviate the spatial hallucination issues of LLMs in spatial reasoning such as discontinuous path planning and out-of-bound errors, a linguistic topological map reconstruction method is designed, improving path generation accuracy by 23.4% in complex obstacle environments compared to conventional spatial representations. In addition, a dual closed-loop feedback strategy is proposed, consisting of model self-correction feedback and camera-based image feedback, which enables real-time correction of routing deviations and enhances operational stability. Experimental results demonstrate that the proposed framework achieves 100% task success across various droplet distribution scenarios, significantly advancing the intelligence of microfluidic operations and providing a promising foundation for fully autonomous DMF systems.
BibTeX
@inproceedings{ral2026_towardintelligen,
title = {Toward Intelligent Microfluidics: An LLM-Based Droplets Controlling Framework},
author = {Fangdi Li and Zelin Wang and Dianhua Zhang and Yuke Pan and Jianguang Zhou},
booktitle = {RA-L 2026},
year = {2026}
}