Embodied AI: A Foundation for Intelligent and Autonomous Manufacturing

Jianjing Zhang a; Lihui Wang b; Robert X. Gao a

doi:10.1016/j.eng.2025.12.026

Engineering ›› :202512026 DOI: 10.1016/j.eng.2025.12.026

Views & Comments

research-article

Embodied AI: A Foundation for Intelligent and Autonomous Manufacturing

Author information +

History +

PDF (652KB)

Abstract

Embodied artificial intelligence (AI) represents a paradigm shift in the design and construction of intelligent physical systems, where emphasis is placed on the integration of physical interaction and situational awareness of such systems to facilitate adaptive decision-making. This embodiment is realized through an interplay of sensing, control, and actuation, where AI not only interprets data but directly interacts with physical processes. Enabled by advances in deep learning (DL), sensing technologies, and computational infrastructure, embodied AI systems permit new avenues for a broad range of manufacturing applications by building upon a series of transformative capabilities that span semantic data inference, learning-based control, and generative optimization of actuation hardware design. This paper presents an overview of recent advances to enable embodied AI for manufacturing and outlines promising research directions.

Keywords

Artificial intelligence / Sensing / Learning / Adaptivity / Autonomous manufacturing

Cite this article

Download citation ▾

Jianjing Zhang a, Lihui Wang b, Robert X. Gao a. Embodied AI: A Foundation for Intelligent and Autonomous Manufacturing. Engineering 202512026 DOI:10.1016/j.eng.2025.12.026

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Duan J, Yu S, Tan HL, Zhu H, Tan C. A survey of embodied AI: from simulators to research tasks. IEEE Trans Emerg Top Comput Intell 2022; 6(2):230-44.

[2]	Pfeifer R, Iida F. Embodied artificial intelligence:trends and challenges. In: Iida F, Pfeifer R, Steels L, Kuniyoshi Y, Embodied artificial intelligence. Berlin: Springer; 2004. p. 1-26.

[3]	Tomizuka M. Mechatronics: from the 20th to 21st century. Control Eng Pract 2002; 10(8):877-86.

[4]	Gao RX, Wang L, Helu M, Teti R. Big data analytics for smart factories of the future. CIRP Ann 2020; 69(2):668-92.

[5]	Gao R, Krüger J, Merklein M, Möhring HC, Váncza J. Artificial Intelligence in manufacturing: state of the art, perspectives, and future directions. CIRP Ann 2024; 73(2):723-49.

[6]	Zhang J, Gao R. Data-enhanced sensing: a new approach to information acquisition. IEEE Trans Instrum Meas 2025; 74:1-20.

[7]	LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521:436-44.

[8]	Pandiyan V, Tjahjowidodo T. In-process endpoint detection of weld seam removal in robotic abrasive belt grinding process. Int J Adv Manuf Technol 2017; 93(5-8):1699-714.

[9]	Li Z, Dekel T, Cole F, Tucker R, Snavely N, Liu C, et al. Mannequin Challenge: learning the depths of moving people by watching frozen people. IEEE Trans Pattern Anal Mach Intell 2021; 43(12):4229-41.

[10]	Liu S, Zhang J, Wang L, Gao R. Vision AI-based human-robot collaborative assembly driven by autonomous robots. CIRP Ann 2024; 73(1):13-6.

[11]	Yang WT, Tomizuka M. Design a multifunctional soft tactile sensor enhanced by machine learning approaches. J Dyn Syst Meas Control 2022; 144(8):081006.

[12]	Truby RL, Santina CD, Rus D. Distributed proprioception of 3D configuration in soft, sensorized robots via deep learning. IEEE Robot Autom Lett 2020; 5(2):3299-306.

[13]	Chen YP, Karkaria V, Tsai YK, Rolark F, Quispe D, Gao R, et al. Real-time decision-making for digital twin in additive manufacturing with model predictive control using time-series deep neural networks. J Manuf Syst 2025; 80:412-24.

[14]	Sutton RS, Barto AG.Reinforcement learning: an introduction. 2nd ed. Cambridge: The MIT Press; 2018.

[15]	Ha H, Agrawal S, Song S. Fit2Form: 3D generative model for robot gripper form design. In: Proceedings of the Conference on Robot Learning; 2020 Nov 16-18; Cambridge, MA, USA. PMLR; 2021. p. 176-87.

[16]	Ji Z, Chen C, Zhu S, Ma Y, Guan X. Intelligent edge sensing and control co-design for industrial cyber-physical system. IEEE Trans Signal Inf Process Netw 2023; 9:175-89.

[17]	Koren Y, Gu X, Guo W. Reconfigurable manufacturing systems: principles, design, and future trends. Front Mech Eng 2018; 13(2):121-36.

[18]	Skoury L, Treml S, Opgenorth N, Amtsberg F, Wagner HJ, Menges A, et al. Towards data-informed co-design in digital fabrication. Auto Constr 2024; 158:105229.

[19]	Schleich B, Anwer N, Mathieu L, Wartzack S. Shaping the digital twin for design and production engineering. CIRP Ann 2017; 66(1):141-4.

[20]	Wang J, Ye L, Gao RX, Li C, Zhang L. Digital Twin for rotating machinery fault diagnosis in smart manufacturing. Int J Prod Res 2019; 57:3920-34.

[21]	Wang J, Niu X, Gao RX, Huang Z, Xue R. Digital twin-driven virtual commissioning of machine tool. Robot Comput Integr Manuf 2023; 81:102499.

[22]	Jiang Y, Gupta A, Zhang Z, Wang G, Dou Y, Chen Y, et al. VIMA:general robot manipulation with multimodal prompts. In:Proceedings of the 40th International Conference on Machine Learning; 2023 Jul 23-29; Honolulu, HI, USA. New York City:PMLR; 2023. p. 14975-5022.

[23]	Song D, Chen X.CS294/194-196 Large Language Model Agents. Report. Berkeley: University of California, Berkeley; 2025.

[24]	Ni M, Wang T, Leng J, Chen C, Cheng L. A large language model-based manufacturing process planning approach under Industry 5.0. Int J Prod Res In press.

[25]

Xia Y, Shenoy M, Jazdi N, Weyrich M. Towards autonomous system:flexible modular production system enhanced with large language model agents. In:Proceedings of the 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA); 2023 Ssp 12-15; Sinaia, Romania. Piscataway: IEEE; 2023. p. 1-8.

[26]	Liu S, Zhang J, Gao RX, Wang XV, Wang L. Vision-language model-driven scene understanding and robotic object manipulation. In:Proceedings of the 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE); 2024 Aug 28-Sep 01; Bari, Italy. Piscataway: IEEE; 2024. p. 21-6.

[27]	Wang B, Zheng P, Yin Y, Shih A, Wang L. Toward human-centric smart manufacturing: a human-cyber-physical systems (HCPS) perspective. J Manuf Syst 2022; 63:471-90.