Electromagnetic metamaterial agent

Shengguo Hu; Mingyi Li; Jiawen Xu; Hongrui Zhang; Shanghang Zhang; Tie Jun Cui; Philipp del Hougne; Lianlin Li

doi:10.1038/s41377-024-01678-w

Go to Nature Website

Your Location：

Home >

Browse articles >

Electromagnetic metamaterial agent

Original Articles

- Electromagnetic metamaterial agent
- Light: Science & Applications Vol. 14, Issue 1, Pages: 118-130(2025)
- Affiliations：
  
  1.State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, Peking University, Beijing 100871, China
  2.National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing 100871, China
  3.State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China
  4.Pazhou Laboratory (Huangpu), Guangzhou, Guangdong 510555, China
  5.Univ Rennes, CNRS, IETR - UMR 6164, F-35000 Rennes, France
- Author bio：
  
  Lianlin Li (lianlin.li@pku.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01678-w
  CLC：
- Published：31 January 2025，
  
  Published Online：01 January 2025，
  
  Received：09 July 2024，
  
  Revised：24 October 2024，
  
  Accepted：2024-10-28
- Accepted：
Scan QR Code
Hu, S. G. et al. Electromagnetic metamaterial agent. Light: Science & Applications, 14, 118-130 (2025).
DOI：

Hu, S. G. et al. Electromagnetic metamaterial agent. Light: Science & Applications, 14, 118-130 (2025). DOI： 10.1038/s41377-024-01678-w.

摘要

Abstract

Metamaterials have revolutionized wave control; in the last two decades

they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified functionality. Although deep-learning techniques play an increasingly important role in metamaterial inverse design

measurement post-processing and end-to-end optimization

their role is ultimately still limited to approximating specific mathematical relations; the metamaterial is still limited to serving as proxy of a human operator

realizing a predefined functionality. Here

we propose and experimentally prototype a paradigm shift toward a metamaterial agent (coined metaAgent) endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks

including electromagnetic (EM) field manipulations and interactions with robots and humans. Leveraging recently released foundation models

metaAgent reasons in high-level natural language

acting upon diverse prompts from an evolving complex environment. Specifically

metaAgent's cerebrum performs high-level task planning in natural language via a multi-agent discussion mechanism

where agents are domain experts in sensing

planning

grounding

and coding. In response to live environmental feedback within a real-world setting emulating an ambient-assisted living context (including human requests in natural language)

our metaAgent prototype self-organizes a hierarchy of EM manipulation tasks in conjunction with commanding a robot. metaAgent masters foundational EM manipulation skills related to wireless communications and sensing

and it memorizes and learns from past experience based on human feedback.

关键词

Keywords

references

Pendry, J. B. et al. Extremely low frequency plasmons in metallic mesostructures. Phys. Rev. Lett. 76 , 4773–4776 (1996)..

Smith, D. R. et al. Composite medium with simultaneously negative permeability and permittivity. Phys. Rev. Lett. 84 , 4184–4187 (2000)..

Shelby, R. A., Smith, D. R. & Schultz, S. Experimental verification of a negative index of refraction. Science 292 , 77–79 (2001)..

Grbic, A. & Eleftheriades, G. V. Overcoming the diffraction limit with a planar left-handed transmission-line lens. Phys. Rev. Lett. 92 , 117403 (2004)..

Yu, N. F. et al. Light propagation with phase discontinuities: generalized laws of reflection and refraction. Science 334 , 333–337 (2011)..

Schurig, D. et al. Metamaterial electromagnetic cloak at microwave frequencies. Science 314 , 977–9 80 (2006)..

Liu, R. et al. Broadband ground-plane cloak. Science 323 , 366–369 (2009)..

Ergin, T. et al. Three-dimensional invisibility cloak at optical wavelengths. Science 328 , 337–339 (2010)..

Pendry, J. B. Negative refraction makes a perfect lens. Phys. Rev. Lett. 85 , 3966–3969 (2000)..

Fang, N. et al. Sub-diffraction-limited optical imaging with a silver superlens. Science 308 , 534–537 (2005)..

Della Giovampaola, C. & Engheta, N. Digital metamaterials. Nat. Mater. 13 , 1115–1121 (2014)..

Cui, T. J. et al. Coding metamaterials, digital metamaterials and programmable metamaterials. Light Sci. Appl. 3 , e218 (2014)..

Zhang, L. et al. Space-time-coding digital metasurfaces. Nat. Commun. 9 , 4334 (2018)..

Ma, Q. et al. Smart metasurface with self-adaptively reprogrammable functions. Light Sci. Appl. 8 , 98 (2019)..

Ma, Q. et al. Smart sensing metasurface with self-defined functions in dual polarizations. Nanophotonics 9 , 3271–3278 (2020)..

Alamzadeh, I. et al. A reconfigurable intelligent surface with integrated sensing capability. Sci. Rep. 11 , 20737 (2021)..

Saigre-Tardif, C. & Del Hougne, P. Self-adaptive RISs beyond free space: convergence of localization, sensing, and communication under rich-scattering conditions. IEEE Wirel. Commun. 30 , 24–30 (2023)..

Zhao, H. T. et al. Intelligent indoor metasurface robotics. Natl Sci. Rev. 10 , nwac266 (2023)..

Alexandropoulos, G. C. et al. Hybrid reconfigurable intelligent metasurfaces: enabling simultaneous tunable reflections and sensing for 6G wireless communications. IEEE Vehicular Technol. Mag. 19 , 75–84 (2024)..

Peurifoy, J. et al. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv. 4 , eaar4206 (2018)..

Wiecha, P. R. et al. Deep learning in Nano-photonics: inverse design and beyond. Photonics Res. 9 , B182–B200 (2021)..

Khatib, O. et al. Deep learning the electromagnetic properties of metamaterials—a comprehensive review. Adv. Funct. Mater. 31 , 2101748 (2021)..

Del Hougne, P. et al. Learned integrated sensing pipeline: reconfigurable metasurface transceivers as trainable physical layer in an artificial neural network. Adv. Sci. 7 , 1901913 (2020)..

Li, H. Y. et al. Intelligent electromagnetic sensing with learnable data acquisition and processing. Patterns 1 , 100006 (2020)..

Qian, C. Q. & Del Hougne, P. Noise-adaptive intelligent programmable meta-imager. Intell. Comput. 2022 , 9825738 (2022)..

Arya, G. et al. End-to-end optimization of metasurfaces for imaging with compressed sensing. ACS Photonics 11 , 2077–2087 (2024)..

Jing, Y. X. et al. A deep neural network for general scattering matrix. Nanophotonics 12 , 2583–2591 (2023)..

Wang, C. et al. Programmable flip-metasurface with dynamically tunable reflection and broadband undistorted transmission. Nanophotonics 13 , 2151–2159 (2024)..

Bommasani, R. et al. On the opportunities and risks of foundation models. arXiv https://doi.org/10.48550/arXiv.2108.07258 https://doi.org/10.48550/arXiv.2108.07258 (2022)..

Radford, A. et al. Improving Language Understanding by Generative Pre-Training . https://cdn.openai.com/research-covers/language-unsupervised/ https://cdn.openai.com/research-covers/language-unsupervised/ (2018)..

Radford, A. Language Models are Unsupervised Multitask Learners. https://cdn.openai.com/better-language-models/ https://cdn.openai.com/better-language-models/ (2019)..

OpenAI et al. GPT-4 technical report. arXiv https://doi.org/10.48550/arXiv.2303.08774 https://doi.org/10.48550/arXiv.2303.08774 (2024)..

Devlin, J. et al. BERT: pre-training of deep bidirectional transformers for language understanding. Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171–4186 (ACL, 2019).

Gemma Team. Gemma: open models based on Gemini research and technology. arXiv https://doi.org/10.48550/arXiv.2403.08295 https://doi.org/10.48550/arXiv.2403.08295 (2024)..

Touvron, H. et al. LLaMA: open and efficient foundation language models. arXiv https://doi.org/10.48550/arXiv.2302.13971 https://doi.org/10.48550/arXiv.2302.13971 (2023)..

Jiang, A. Q. et al. Mistral 7B. arXiv https://doi.org/10.48550/arXiv.2310.06825 https://doi.org/10.48550/arXiv.2310.06825 (2023)..

Brown, T. B. et al. Language models are few-shot learners. Proc. 34th International Conference on Neural Information Processing Systems 159 (ACM, 2020).

Vemprala, S. H. et al. ChatGPT for robotics: design principles and model abilities. IEEE Access 12 , 55682–55696 (2024)..

Ichter, B. et al. Do as I can, not as I say: grounding language in robotic affordances. 6th Conference on Robot Learning. 287–318 (PMLR, 2022).

Liang, J. et al. Code as policies: language model programs for embodied control. 2023 IEEE International Conference on Robotics and Automation (ICRA) 9493–9500 (IEEE, 2023).

Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41 , 1099–1106 (2023)..

Choi, J. & Lee, B. Accelerating materials language processing with large language models. Commun. Mater. 5 , 13 (2024)..

Zhang, H. R. et al. Semantic regularization of electromagnetic inverse problems. Nat. Commun. 15 , 3869 (2024)..

Lu, D. R. et al. Can large language models learn the physics of metamaterials? an empirical study with ChatGPT. arXiv http://export.arxiv.org/abs/2404.15458 http://export.arxiv.org/abs/2404.15458 (2024)..

Hu, S. G. et al. Language-controllable programmable metasurface empowered by large language models. Nanophotonics 13 , 2213–2222 (2024)..

Vaswani, A. et al. Attention is all you need. Proc. 31st International Conference on Neural Information Processing Systems. 6000–6010 (CA, USA: ACM, 2017).

Wu, Q. Y. et al. AutoGen: enabling next-gen LLM applications via multi-agent conversation. arXiv https://doi.org/10.48550/arXiv.2308.08155 https://doi.org/10.48550/arXiv.2308.08155 (2023)..

Li, L. L. et al. Electromagnetic reprogrammable coding-metasurface holograms. Nat. Commun. 8 , 197 (2017)..

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰