AI-driven robotics for optics

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Authors: Shiekh Zia Uddin, Sachin Vaidya, Shrish Choudhary, Zhuo Chen, Raafat K. Salib, Luke Huang, Dirk Englund, Marin Soljačić
Cited by: 0

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.

Fields of Study & Subjects

Neural Networks and Reservoir Computing Orbital Angular Momentum in Optics Machine Learning in Materials Science

Publication details

Journal / source
Science Advances
Volume
12
Issue
34
Pages
eaee1381–eaee1381
Publication date
19 August 2026
Language
EN