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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.17985 |
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| _version_ | 1866915619579887616 |
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| author | Uddin, Shiekh Zia Vaidya, Sachin Choudhary, Shrish Chen, Zhuo Salib, Raafat K. Huang, Luke Englund, Dirk R. Soljačić, Marin |
| author_facet | Uddin, Shiekh Zia Vaidya, Sachin Choudhary, Shrish Chen, Zhuo Salib, Raafat K. Huang, Luke Englund, Dirk R. Soljačić, Marin |
| contents | Optics is foundational to research in many areas of science and engineering, including nanophotonics, quantum information, materials science, biomedical imaging, and metrology. However, the design, assembly, and alignment of optical experiments remain predominantly manual, limiting throughput and reproducibility. Automating such experiments is challenging due to the strict, non-negotiable precision requirements and the diversity of optical configurations found in typical laboratories. Here, we introduce a platform that integrates generative artificial intelligence, computer vision, and robotics to automate free-space optical experiments. The platform translates user-defined goals into valid optical configurations, assembles them using a robotic arm, and performs micrometer-scale fine alignment using a robot-deployable tool. It then executes a range of automated measurements, including beam characterization, polarization mapping, and spectroscopy, with consistency surpassing that of human operators. This work demonstrates the first flexible, AI-driven automation platform for optics, offering a path towards remote operation, cloud labs, and high-throughput discovery in the optical sciences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Driven Robotics for Optics Uddin, Shiekh Zia Vaidya, Sachin Choudhary, Shrish Chen, Zhuo Salib, Raafat K. Huang, Luke Englund, Dirk R. Soljačić, Marin Optics Robotics Optics is foundational to research in many areas of science and engineering, including nanophotonics, quantum information, materials science, biomedical imaging, and metrology. However, the design, assembly, and alignment of optical experiments remain predominantly manual, limiting throughput and reproducibility. Automating such experiments is challenging due to the strict, non-negotiable precision requirements and the diversity of optical configurations found in typical laboratories. Here, we introduce a platform that integrates generative artificial intelligence, computer vision, and robotics to automate free-space optical experiments. The platform translates user-defined goals into valid optical configurations, assembles them using a robotic arm, and performs micrometer-scale fine alignment using a robot-deployable tool. It then executes a range of automated measurements, including beam characterization, polarization mapping, and spectroscopy, with consistency surpassing that of human operators. This work demonstrates the first flexible, AI-driven automation platform for optics, offering a path towards remote operation, cloud labs, and high-throughput discovery in the optical sciences. |
| title | AI-Driven Robotics for Optics |
| topic | Optics Robotics |
| url | https://arxiv.org/abs/2505.17985 |