AI-driven robotics for optics

Optical experiments are essential across science and technology, yet their design, assembly, and alignment remain predominantly manual, limiting throughput, reproducibility, and scalability. Automating such experiments is challenging because of stringent precision requirements and the diversity of setups in typical real-world optical laboratory environments. Here, we introduce a platform that integrates generative artificial intelligence, computer vision, and precision robotics to automate free-space optical experiments. The system translates user-defined goals into valid optical configurations, assembles them with submillimeter accuracy, and performs micrometer-scale fine alignment using a robotic tool. It then executes a range of accurate measurements, including beam characterization, polarization mapping, and spectroscopy. This work establishes a platform for reconfigurable optics automation, potentially enabling programmable and adaptive experimental workflows.

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Publication Details

Journal
Science Advances
Published
2026-08-19
DOI
https://doi.org/10.1126/sciadv.aee1381
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

AI-driven robotics for optics

Shiekh Zia Uddin, Sachin Vaidya, Zhuo Chen, Dirk Englund et al.
Science Advances
Neural Networks and Reservoir Computing
article

AI-driven robotics for optics

Shiekh Zia Uddin, Sachin Vaidya, Zhuo Chen, Dirk Englund, Marin Soljačić, Raafat K. Salib
article en

Abstract

Optical experiments are essential across science and technology, yet their design, assembly, and alignment remain predominantly manual, limiting throughput, reproducibility, and scalability. Automating such experiments is challenging because of stringent precision requirements and the diversity of setups in typical real-world optical laboratory environments. Here, we introduce a platform that integrates generative artificial intelligence, computer vision, and precision robotics to automate free-space optical experiments. The system translates user-defined goals into valid optical configurations, assembles them with submillimeter accuracy, and performs micrometer-scale fine alignment using a robotic tool. It then executes a range of accurate measurements, including beam characterization, polarization mapping, and spectroscopy. This work establishes a platform for reconfigurable optics automation, potentially enabling programmable and adaptive experimental workflows.

Science AdvancesVol. 12(34)
IIT@MIT (US), Cambridge Electronics (United States) (US), Data & Society Research Institute (US), The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (US)
National Science Foundation, Army Research Office
Openalex Percentile: Top 98%
Neural Networks and Reservoir Computing
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