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Volume 12 Issue 3
Mar.  2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  >  12(3): 485-501
J. Xu, Q. Sun, Q.-L. Han, and Y. Tang, “When embodied AI meets Industry 5.0: Human-centered smart manufacturing,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 485–501, Mar. 2025. doi: 10.1109/JAS.2025.125327
Citation: J. Xu, Q. Sun, Q.-L. Han, and Y. Tang, “When embodied AI meets Industry 5.0: Human-centered smart manufacturing,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 485–501, Mar. 2025. doi: 10.1109/JAS.2025.125327
shu

When Embodied AI Meets Industry 5.0: Human-Centered Smart Manufacturing

doi: 10.1109/JAS.2025.125327
Funds:  This work was supported by the National Key Research and Development Program of China (2021YFB1714300), the National Natural Science Foundation of China (62233005, U2441245, 62173141), CNPC Innovation Found (2024DQ02-0507), Shanghai Natural Science Foundation Project (24ZR1416400), Shanghai BaiyuLan Talent Program Pujiang Project (24PJD020), and the Programme of Introducing Talents of Discipline to Universities (the 111 Project) (B17017)
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    Abstract
  • As embodied intelligence (EI), large language models (LLMs), and cloud computing continue to advance, Industry 5.0 facilitates the development of industrial artificial intelligence (IndAI) through cyber-physical-social systems (CPSSs) with a human-centric focus. These technologies are organized by the system-wide approach of Industry 5.0, in order to empower the manufacturing industry to achieve broader societal goals of job creation, economic growth, and green production. This survey first provides a general framework of smart manufacturing in the context of Industry 5.0. Wherein, the embodied agents, like robots, sensors, and actuators, are the carriers for IndAI, facilitating the development of the self-learning intelligence in individual entities, the collaborative intelligence in production lines and factories (smart systems), and the swarm intelligence within industrial clusters (systems of smart systems). Through the framework of CPSSs, the key technologies and their possible applications for supporting the single-agent, multi-agent and swarm-agent embodied IndAI have been reviewed, such as the embodied perception, interaction, scheduling, multi-mode large language models, and collaborative training. Finally, to stimulate future research in this area, the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed. The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner, thereby fostering an intelligent, sustainable, and resilient industrial landscape.

     

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    • Jing Xu and Qiyu Sun contributed equally to this work.
    References(131)
  • [1]
    B. Ding, X. F. Hernández, and N. A. Jané, “Combining lean and agile manufacturing competitive advantages through Industry 4.0 technologies: An integrative approach,” Prod. Plann. Control, vol. 34, no. 5, pp. 442–458, 2023.
    [2]
    M. Deitke, W. Han, A. Herrasti, A. Kembhavi, E. Kolve, R. Mottaghi, J. Salvador, D. Schwenk, E. VanderBilt, M. Wallingford, L. Weihs, M. Yatskar, and A. Farhadi, “Robothor: An open simulation-to-real embodied AI platform,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020, pp. 3161–3171.
    [3]
    C. D. Onal and D. Rus, “Autonomous undulatory serpentine locomotion utilizing body dynamics of a fluidic soft robot,” Bioinspir. Biomim., vol. 8, no. 2, p. 026003, Jun. 2013. doi: 10.1088/1748-3182/8/2/026003
    [4]
    S. J. Yoo, J. B. Park, and Y. H. Choi, “Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks,” IEEE Trans. Syst. Man Cybern. Part B (Cybern.), vol. 36, no. 6, pp. 1342–1355, Dec. 2006. doi: 10.1109/TSMCB.2006.875869
    [5]
    M. Hao, H. Li, X. Luo, G. Xu, H. Yang, and S. Liu, “Efficient and privacy-enhanced federated learning for industrial artificial intelligence,” IEEE Trans. Ind. Inf., vol. 16, no. 10, pp. 6532–6542, Oct. 2020. doi: 10.1109/TII.2019.2945367
    [6]
    S. Zhu, K. Ota, and M. Dong, “Green AI for IIoT: Energy efficient intelligent edge computing for industrial internet of things,” IEEE Trans. Green Commun. Netw., vol. 6, no. 1, pp. 79–88, Mar. 2022. doi: 10.1109/TGCN.2021.3100622
    [7]
    W. Xiang, K. Yu, F. Han, L. Fang, D. He, and Q.-L. Han, “Advanced manufacturing in Industry 5.0: A survey of key enabling technologies and future trends,” IEEE Trans. Ind. Inf., vol. 20, no. 2, pp. 1055–1068, Feb. 2024. doi: 10.1109/TII.2023.3274224
    [8]
    J. Leng, W. Sha, B. Wang, P. Zheng, C. Zhuang, Q. Liu, T. Wuest, D. Mourtzis, and L. Wang, “Industry 5.0: Prospect and retrospect,” J. Manuf. Syst., vol. 65, pp. 279–295, Oct. 2022. doi: 10.1016/j.jmsy.2022.09.017
    [9]
    F.-Y. Wang, “Plenary sessions plenary talk I: “The origin and goal of future in CPSS: Industries 4.0 and Industries 5.0”,” in Proc. IEEE Int. Conf. Systems, Man and Cybernetics, Bari, Italy, 2019, pp. 1–3.
    [10]
    O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, and X. Wang, “Internet of things for the future of smart agriculture: A comprehensive survey of emerging technologies,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 718–752, Apr. 2021. doi: 10.1109/JAS.2021.1003925
    [11]
    M. Zhou, “Editorial: Evolution from AI, IoT and big data analytics to metaverse,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2041–2042, Dec. 2022. doi: 10.1109/JAS.2022.106100
    [12]
    F.-Y. Wang, “The emergence of intelligent enterprises: From CPS to CPSS,” IEEE Intell. Syst., vol. 25, no. 4, pp. 85–88, Jul.–Aug. 2010. doi: 10.1109/MIS.2010.104
    [13]
    L. Vlacic, H. Huang, M. Dotoli, Y. Wang, P. A. Ioannou, L. Fan, X. Wang, R. Carli, C. Lv, L. Li, X. Na, Q.-L. Han, and F.-Y. Wang, “Automation 5.0: The key to systems intelligence and Industry 5.0,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1723–1727, Aug. 2024. doi: 10.1109/JAS.2024.124635
    [14]
    X. Xu, Y. Q. Lu, B. Vogel-Heuser, and L. Wang, “Industry 4.0 and Industry 5.0—Inception, conception and perception,” J. Manuf. Syst., vol. 61, pp. 530–535, 2021. doi: 10.1016/j.jmsy.2021.10.006
    [15]
    C. Zhang, Z. Wang, G. Zhou, F. Chang, D. Ma, Y. Jing, W. Cheng, K. Ding, and D. Zhao, “Towards new-generation human-centric smart manufacturing in Industry 5.0: A systematic review,” Adv. Eng. Inf., vol. 57, p. 102121, Aug. 2023. doi: 10.1016/j.aei.2023.102121
    [16]
    Y. Yang, T. Zhou, K. Li, D. Tao, L. Li, L. Shen, X. He, J. Jiang, and Y. Shi, “Embodied multi-modal agent trained by an LLM from a parallel TextWorld,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, WA, USA, 2024, pp. 26265–26275.
    [17]
    B. Goertzel and C. Pennachin, Artificial General Intelligence. Berlin, Germany: Springer, 2007.
    [18]
    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. R. Narasimhan, and Y. Cao, “ReAct: Synergizing reasoning and acting in language models,” in Proc. 11th Int. Conf. Learning Representations, Kigali, Rwanda, 2023.
    [19]
    N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: Language agents with verbal reinforcement learning,” in Proc. 37th Conf. Neural Information Processing Systems, New Orleans, LA, USA, 2023, pp. 8634–8652.
    [20]
    J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” in Proc. 36th Conf. Neural Information Processing Systems, New Orleans, LA, USA, 2022, pp. 24824–24837.
    [21]
    T. Schick, J. Dwivedi-Yu, R. Dessí, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda, and T. Scialom, “Toolformer: Language models can teach themselves to use tools,” in Proc. 37th Conf. Neural Information Processing Systems, New Orleans, LA, USA, 2023, pp. 68539–68551.
    [22]
    S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” in Proc. 37th Int. Conf. Neural Information Processing Systems, New Orleans, LA, USA, 2023, pp. 517.
    [23]
    J. Duan, S. Yu, H. L. Tan, H. Zhu, and C. Tan, “A survey of embodied AI: From simulators to research tasks,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 6, no. 2, pp. 230–244, Apr. 2022. doi: 10.1109/TETCI.2022.3141105
    [24]
    Y. Song, P. Sun, H. Liu, Z. Li, W. Song, Y. Xiao, and X. Zhou, “Scene-driven multimodal knowledge graph construction for embodied AI,” IEEE Trans. on Knowl. Data Eng., vol. 36, no. 11, pp. 6962–6976, Nov. 2024. doi: 10.1109/TKDE.2024.3399746
    [25]
    Y. Huo, M. Zhang, G. Liu, H. Lu, Y. Gao, G. Yang, J. Wen, H. Zhang, B. Xu, W. Zheng, Z. Xi, Y. Yang, A. Hu, J. Zhao, R. Li, Y. Zhao, L. Zhang, Y. Song, X. Hong, W. Cui, D. Hou, Y. Li, J. Li, P. Liu, Z. Gong, C. Jin, Y. Sun, S. Chen, Z. Lu, Z. Dou, Q. Jin, Y. Lan, W. X. Zhao, R. Song, and J.-R. Wen, “WenLan: Bridging vision and language by large-scale multi-modal pre-training,” arXiv preprint arXiv: 2103.06561, 2021.
    [26]
    S. Belkhale, T. Ding, T. Xiao, P. Sermanet, Q. Vuong, J. Tompson, Y. Chebotar, D. Dwibedi, and D. Sadigh, “RT-H: Action hierarchies using language,” in Proc. Robotics: Science and Systems XX, Delft, The Netherlands, 2024.
    [27]
    X. Li, M. Zhang, Y. Geng, H. Geng, Y. Long, Y. Shen, R. Zhang, J. Liu, and H. Dong, “ManipLLM: Embodied multimodal large language model for object-centric robotic manipulation,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, WA, USA, 2024, pp. 18061–18070.
    [28]
    H. Zhang, W. Du, J. Shan, Q. Zhou, Y. Du, J. B. Tenenbaum, T. Shu, and C. Gan, “Building cooperative embodied agents modularly with large language models,” in Proc. 12th Int. Conf. Learning Representations, Vienna, Austria, 2024.
    [29]
    L. Hu, Y. Miao, G. Wu, M. M. Hassan, and I. Humar, “iRobot-factory: An intelligent robot factory based on cognitive manufacturing and edge computing,” Future Gener. Comput. Syst., vol. 90, pp. 569–577, Jan. 2019. doi: 10.1016/j.future.2018.08.006
    [30]
    Y. Tong, H. Liu, and Z. Zhang, “Advancements in humanoid robots: A comprehensive review and future prospects,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 301–328, Feb. 2024. doi: 10.1109/JAS.2023.124140
    [31]
    W. Ren, Y. Tang, Q. Sun, C. Zhao, and Q.-L. Han, “Visual semantic segmentation based on few/zero-shot learning: An overview,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 5, pp. 1106–1126, May 2024. doi: 10.1109/JAS.2023.123207
    [32]
    Q. Sun, G. G. Yen, Y. Tang, and C. Zhao, “Learn to adapt for self-supervised monocular depth estimation,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 11, pp. 15647–15659, Nov. 2024. doi: 10.1109/TNNLS.2023.3289051
    [33]
    S. Feng, L. Zeng, J. Liu, Y. Yang, and W. Song, “Multi-UAVs collaborative path planning in the cramped environment,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 529–538, Feb. 2024. doi: 10.1109/JAS.2023.123945
    [34]
    Q. Sun, J. Fang, W. X. Zheng, and Y. Tang, “Aggressive quadrotor flight using curiosity-driven reinforcement learning,” IEEE Trans. Ind. Electron., vol. 69, no. 12, pp. 13838–13848, Dec. 2022. doi: 10.1109/TIE.2022.3144586
    [35]
    Y. Tang, C. Zhao, J. Wang, C. Zhang, Q. Sun, W. X. Zheng, W. Du, F. Qian, and J. Kurths, “Perception and navigation in autonomous systems in the era of learning: A survey,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 12, pp. 9604–9624, Dec. 2023. doi: 10.1109/TNNLS.2022.3167688
    [36]
    J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, USA, 2019, pp. 4171–4186.
    [37]
    J. Achiam, S. Adler, S. Agarwal, et al., “GPT-4 Technical Report,” arXiv preprint arXiv: 2303.08774, 2024.
    [38]
    J. Li, D. Li, C. Xiong, and S. Hoi, “BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in Proc. 39th Int. Conf. Machine Learning, Baltimore, MD, USA, 2022, pp. 12888–12900.
    [39]
    J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in Proc. 40th Int. Conf. Machine Learning, Honolulu, HI, USA, 2023, p. 814.
    [40]
    T. Wu, S. He, J. Liu, S. Sun, K. Liu, Q.-L. Han, and Y. Tang, “A brief overview of ChatGPT: The history, status quo and potential future development,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1122–1136, May 2023. doi: 10.1109/JAS.2023.123618
    [41]
    Z. Yang, L. Li, K. Lin, J. Wang, C.-C. Lin, Z. Liu, and L. Wang, “The dawn of LMMs: Preliminary explorations with GPT-4V(ision),” arXiv preprint arXiv: 2309.17421, 2023.
    [42]
    H. Huang, F. Lin, Y. Hu, S. Wang, and Y. Gao, “CoPa: General robotic manipulation through spatial constraints of parts with foundation models,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Abu Dhabi, United Arab Emirates, 2024, pp. 9488–9495.
    [43]
    Q. Yu, C. Hao, J. Wang, W. Liu, L. Liu, Y. Mu, Y. You, H. Yan, and C. Lu, “ManiPose: A comprehensive benchmark for pose-aware object manipulation in robotics,” arXiv preprint arXiv: 2403.13365, 2024.
    [44]
    B. Zitkovich, T. Yu, S. Xu, P. Xu, T. Xiao, F. Xia, J. Wu, P. Wohlhart, S. Welker, A. Wahid, Q. Vuong, V. Vanhoucke, H. Tran, R. Soricut, A. Singh, J. Singh, P. Sermanet, P. R. Sanketi, G. Salazar, M. S. Ryoo, K. Reymann, K. Rao, K. Pertsch, I. Mordatch, H. Michalewski, Y. Lu, S. Levine, L. Lee, T.-W. E. Lee, I. Leal, Y. Kuang, D. Kalashnikov, R. Julian, N. J. Joshi, A. Irpan, B. Ichter, J. Hsu, A. Herzog, K. Hausman, K. Gopalakrishnan, C. Fu, P. Florence, C. Finn, K. A. Dubey, D. Driess, T. Ding, K. M. Choromanski, X. Chen, Y. Chebotar, J. Carbajal, N. Brown, A. Brohan, M. G. Arenas, and K. Han, “RT-2: Vision-language-action models transfer web knowledge to robotic control,” in Proc. 7th Conf. Robot Learning, Atlanta, GA, USA, 2023, pp. 2165–2183.
    [45]
    F. Liu, K. Fang, P. Abbeel, and S. Levine, “MOKA: Open-vocabulary robotic manipulation through mark-based visual prompting,” arXiv preprint arXiv: 2403.03174, 2024.
    [46]
    P. Liu, Y. Orru, J. Vakil, C. Paxton, N. M. M. Shafiullah, and L. Pinto, “OK-Robot: What really matters in integrating open-knowledge models for robotics,” arXiv preprint arXiv: 2401.12202, 2024.
    [47]
    Y. Qian, X. Zhu, O. Biza, S. Jiang, L. Zhao, H. Huang, Y. Qi, and R. Platt, “ThinkGrasp: A vision-language system for strategic part grasping in clutter,” arXiv preprint arXiv: 2407.11298, 2024.
    [48]
    C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in Proc. Robotics: Science and Systems XIX, Daegu, Korea (South), 2023.
    [49]
    H. Zhen, X. Qiu, P. Chen, J. Yang, X. Yan, Y. Du, Y. Hong, and C. Gan, “3D-VLA: A 3D vision-language-action generative world model,” in Proc. 41st Int. Conf. Machine Learning, Vienna, Austria, 2024.
    [50]
    J. Zhang, L. Tang, Y. Song, Q. Meng, H. Qian, J. Shao, W. Song, S. Zhu, and J. Gu, “FLTRNN: Faithful long-horizon task planning for robotics with large language models,” in Proc. IEEE Int. Conf. Robotics and Automation, Yokohama, Japan, 2024, pp. 6680–6686.
    [51]
    B. Y. Lin, Y. Fu, K. Yang, F. Brahman, S. Huang, C. Bhagavatula, P. Ammanabrolu, Y. Choi, and X. Ren, “SWIFTSAGE: A generative agent with fast and slow thinking for complex interactive tasks,” in Proc. 37th Int. Conf. Neural Information Processing Systems, New Orleans, LA, USA, 2023, pp. 1034.
    [52]
    K. N. Kumar, I. Essa, and S. Ha, “Graph-based cluttered scene generation and interactive exploration using deep reinforcement learning,” in Proc. Int. Conf. Robotics and Automation, Philadelphia, PA, USA, 2022, pp. 7521–7527.
    [53]
    M. Zhu, Y. Zhu, J. Li, J. Wen, Z. Xu, Z. Che, C. Shen, Y. Peng, D. Liu, F. Feng, and J. Tang, “Language-conditioned robotic manipulation with fast and slow thinking,” in Proc. IEEE Int. Conf. Robotics and Automation, Yokohama, Japan, 2024, pp. 4333–4339.
    [54]
    X. Ning, Z. Lin, Z. Zhou, Z. Wang, H. Yang, and Y. Wang, “Skeleton-of-thought: Prompting LLMs for efficient parallel generation,” in Proc. 12th Int. Conf. Learning Representations, Vienna, Austria, 2024.
    [55]
    G. Xiao, J. Lin, M. Seznec, H. Wu, J. Demouth, and S. Han, “SmoothQuant: Accurate and efficient post-training quantization for large language models,” in Proc. 40th Int. Conf. Machine Learning, Honolulu, HI, USA, 2023, pp. 1585.
    [56]
    E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh, “GPTQ: Accurate post-training quantization for generative pre-trained transformers,” arXiv preprint arXiv: 2210.17323, 2023.
    [57]
    Y. Sheng, L. Zheng, B. Yuan, Z. Li, M. Ryabinin, B. Chen, P. Liang, C. Ré, I. Stoica, and C. Zhang, “FlexGen: High-throughput generative inference of large language models with a single GPU,” in Proc. 40th Int. Conf. Machine Learning, Honolulu, HI, USA, 2023, p. 1288.
    [58]
    H. Wang, Z. Zhang, and S. Han, “SpAtten: Efficient sparse attention architecture with cascade token and head pruning,” in Proc. IEEE Int. Symp. on High-Performance Computer Architecture, Seoul, Korea (South), 2021, p. 97–110.
    [59]
    N. Kitaev, Ł. Kaiser, and A. Levskaya, “Reformer: The efficient transformer,” in Proc. 8th Int. Conf. Learning Representations, Addis Ababa, Ethiopia, 2020.
    [60]
    S. Wang, B. Z. Li, M. Khabsa, H. Fang, and H. Ma, “Linformer: Self-attention with linear complexity,” arXiv preprint arXiv: 2006.04768, 2020.
    [61]
    T. Dao, D. Fu, S. Ermon, A. Rudra, and C. Ré, “Flashattention: Fast and memory-efficient exact attention with IO-awareness,” in Proc. 36th Conf. Neural Information Processing Systems, New Orleans, LA, USA, 2022, pp. 16344–16359.
    [62]
    T. He, Y. Liu, Y.-S. Ong, X. Wu, and X. Luo, “Polarized message-passing in graph neural networks,” Artif. Intell., vol. 331, p. 104129, Jun. 2024. doi: 10.1016/j.artint.2024.104129
    [63]
    T. He, Y.-S. Ong, and L. Bai, “Learning conjoint attentions for graph neural nets,” in Proc. 35th Conf. Neural Information Processing Systems, 2021, pp. 2641–2653.
    [64]
    J. Xu, Y. Cai, D. Liu, and Y. Niu, “Model-free formation control: Multi-input iterative learning super-twisting approach,” IEEE Trans. Syst. Man Cybern.: Syst., vol. 54, no. 5, pp. 2765–2774, May 2024. doi: 10.1109/TSMC.2023.3348793
    [65]
    J. Xu, Y. Niu, and H.-K. Lam, “Nonperiodic multirate sampled-data fuzzy control of singularly perturbed nonlinear systems,” IEEE Trans. Fuzzy Syst., vol. 31, no. 9, pp. 2891–2903, Sept. 2023. doi: 10.1109/TFUZZ.2023.3234116
    [66]
    J. Xu, C. Cai, Y. Niu, and H.-K. Lam, “Genetic-algorithm-assisted self-scheduled multidelay PIR control: Experiments in a car-like vehicle system,” IEEE Trans. Cybern., vol. 54, no. 1, pp. 39–49, Jan. 2022.
    [67]
    M. Chen, D. Gündüz, K. Huang, W. Saad, M. Bennis, A. V. Feljan, and H. V. Poor, “Distributed learning in wireless networks: Recent progress and future challenges,” IEEE J. Sel. Areas Commun., vol. 39, no. 12, pp. 3579–3605, Dec. 2021. doi: 10.1109/JSAC.2021.3118346
    [68]
    J. Xu, Y. Niu, and H.-K. Lam, “Adaptive distributed attitude consensus of a heterogeneous multiagent quadrotor system: Singular perturbation approach,” IEEE Trans. Aerosp. Electron. Syst., vol. 59, no. 6, pp. 9722–9732, Dec. 2023. doi: 10.1109/TAES.2023.3264495
    [69]
    J. Xu, Y. Niu, and P. Shi, “Adaptive multi-input super twisting control for a quadrotor: Singular perturbation approach,” IEEE Trans. Ind. Electron., vol. 71, no. 5, pp. 5195–5204, May 2024. doi: 10.1109/TIE.2023.3281686
    [70]
    M. B. Yassein, M. Q. Shatnawi, S. Aljwarneh, and R. Al-Hatmi, “Internet of things: Survey and open issues of MQTT protocol,” in Proc. Int. Conf. Engineering & MIS, Monastir, Tunisia, 2017, pp. 1–6.
    [71]
    E. Seraj, “Enhancing teamwork in multi-robot systems: Embodied intelligence via model- and data-driven approaches,” Ph.D. dissertation, Georgia Institute of Technology, Atlanta, USA, 2023.
    [72]
    D. Weyns, E. Steegmans, and T. Holvoet, “Towards active perception in situated multi-agent systems,” Appl. Artif. Intell., vol. 18, no. 9-10, pp. 867–883, Oct. 2004. doi: 10.1080/08839510490509063
    [73]
    F. Lian, A. Chakrabortty, and A. Duel-Hallen, “Game-theoretic multi-agent control and network cost allocation under communication constraints,” IEEE J. Sel. Areas Commun., vol. 35, no. 2, pp. 330–340, Feb. 2017. doi: 10.1109/JSAC.2017.2659338
    [74]
    H. Qianwei, M. Hongxu, and Z. Hui, “Collision-avoidance mechanism of multi agent system,” in Proc. IEEE Int. Conf. Robotics, Intelligent Systems and Signal Processing, Changsha, China, 2003, pp. 1036–1040.
    [75]
    Z. Liu, B. Wu, J. Dai, and H. Lin, “Distributed communication-aware motion planning for multi-agent systems from STL and SpaTel specifications,” in Proc. IEEE 56th Annu. Conf. Decision and Control, Melbourne, VIC, Australia, 2017, pp. 4452–4457.
    [76]
    L. Canese, G. C. Cardarilli, L. Di Nunzio, R. Fazzolari, D. Giardino, M. Re, and S. Spanó, “Multi-agent reinforcement learning: A review of challenges and applications,” Appl. Sci., vol. 11, no. 11, p. 4948, May 2021. doi: 10.3390/app11114948
    [77]
    M. J. Matarić, “Learning in behavior-based multi-robot systems: Policies, models, and other agents,” Cognit. Syst. Res., vol. 2, no. 1, pp. 81–93, Apr. 2001. doi: 10.1016/S1389-0417(01)00017-1
    [78]
    T.-H. Ho and X. Su, “A dynamic level-k model in sequential games,” Manage. Sci., vol. 59, no. 2, pp. 452–469, Nov. 2013.
    [79]
    Y. Rizk, M. Awad, and E. W. Tunstel, “Cooperative heterogeneous multi-robot systems: A survey,” ACM Comput. Surv. (CSUR), vol. 52, no. 2, p. 29, Apr. 2019.
    [80]
    P. Schillinger, S. García, A. Makris, K. Roditakis, M. Logothetis, K. Alevizos, W. Ren, P. Tajvar, P. Pelliccione, A. Argyros, K. J. Kyriakopoulos, and D. V. Dimarogonas, “Adaptive heterogeneous multi-robot collaboration from formal task specifications,” Robot. Auton. Syst., vol. 145, p. 103866, Nov. 2021. doi: 10.1016/j.robot.2021.103866
    [81]
    R. K. Ramachandran, P. Pierpaoli, M. Egerstedt, and G. S. Sukhatme, “Resilient monitoring in heterogeneous multi-robot systems through network reconfiguration,” IEEE Transactions on Robotics, vol. 38, no. 1, pp. 126–138, 2022. doi: 10.1109/TRO.2021.3128313
    [82]
    Z. Yin, Q. Sun, C. Chang, Q. Guo, J. Dai, X. Huang, and X. Qiu, “Exchange-of-thought: Enhancing large language model capabilities through cross-model communication,” in Proc. Conf. Empirical Methods in Natural Language Processing, Singapore, Singapore, 2023, pp. 15135–15153.
    [83]
    J. Ruan, Y. Chen, B. Zhang, Z. Xu, T. Bao, G. Du, S. Shi, H. Mao, X. Zeng, and R. Zhao, “TPTU: Task planning and tool usage of large language model-based AI agents,” arXiv preprint arXiv: 2308.03427v1, 2023.
    [84]
    J. Wang, Y. Hong, J. Wang, J. Xu, Y. Tang, Q.-L. Han, and J. Kurths, “Cooperative and competitive multi-agent systems: From optimization to games,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 763–783, May 2022. doi: 10.1109/JAS.2022.105506
    [85]
    M. Chen, “Robust tracking control for self-balancing mobile robots using disturbance observer,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 458–465, Jan. 2017. doi: 10.1109/JAS.2017.7510544
    [86]
    R. Anil, A. M. Dai, O. Firat, et al., “PaLM 2 Technical Report,” arXiv preprint arXiv: 2305.10403, 2023.
    [87]
    H. Touvron, L. Martin, K. Stone, et al., “Llama 2: Open foundation and fine-tuned chat models,” arXiv preprint arXiv: 2307.09288, 2023.
    [88]
    Gemini Team and Google, “Gemini: A family of highly capable multimodal models,” arXiv preprint arXiv: 2312.11805, 2024.
    [89]
    S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, and Y. Zhang, “Sparks of artificial general intelligence: Early experiments with GPT-4,” arXiv preprint arXiv: 2303.12712, 2023.
    [90]
    H. Cai and Y. Mostofi, “Human–robot collaborative site inspection under resource constraints,” IEEE Trans. Robot., vol. 35, no. 1, pp. 200–215, Feb. 2019. doi: 10.1109/TRO.2018.2875389
    [91]
    T. Kaupp, A. Makarenko, and H. Durrant-Whyte, “Human–robot communication for collaborative decision making—A probabilistic approach,” Robot. Auton. Syst., vol. 58, no. 5, pp. 444–456, May 2010. doi: 10.1016/j.robot.2010.02.003
    [92]
    V.-T. Ngo and Y.-C. Liu, “Human-robot coordination control for heterogeneous Euler-Lagrange systems under communication delays and relative position,” IEEE Trans. Ind. Electron., vol. 70, no. 2, pp. 1761–1771, Feb. 2023. doi: 10.1109/TIE.2022.3159924
    [93]
    K. Zhang and X. Li, “Human-robot team coordination that considers human fatigue,” Int. J. Adv. Robot. Syst., vol. 11, no. 6, p. 91, Jun. 2014. doi: 10.5772/58228
    [94]
    Y. Wang, K. Zhu, Y. Dai, D. Su, and K. Zhao, “A distributed training quantization strategy for low-speed and unstable network,” in Proc. 5th Int. Seminar on Artificial Intelligence, Networking and Information Technology, Nanjing, China, 2024, pp. 1094–1098.
    [95]
    H. Li, Z. Wang, C. Lan, P. Wu, and N. Zeng, “A novel dynamic multiobjective optimization algorithm with non-inductive transfer learning based on multi-strategy adaptive selection,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 11, pp. 16533–16547, 2024. doi: 10.1109/TNNLS.2023.3295461
    [96]
    H. Li, Z. Wang, N. Zeng, P. Wu, and Y. Li, “Promoting objective knowledge transfer: A cascaded fuzzy system for solving dynamic multiobjective optimization problems,” IEEE Trans. Fuzzy Syst., vol. 32, no. 11, pp. 6199–6213, Nov. 2024. doi: 10.1109/TFUZZ.2024.3443207
    [97]
    Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch, “Improving factuality and reasoning in language models through multiagent debate,” in Proc. 41st Int. Conf. Machine Learning, Vienna, Austria, 2023, p. 467.
    [98]
    Z. Yuan, Y. Liu, Y. Cao, W. Sun, H. Jia, R. Chen, Z. Li, B. Lin, L. Yuan, L. He, C. Wang, Y. Ye, and L. Sun, “Mora: Enabling generalist video generation via a multi-agent framework,” arXiv preprint arXiv: 2403.13248, 2024.
    [99]
    E. Frantar and D. Alistarh, “SparseGPT: Massive language models can be accurately pruned in one-shot,” in Proc. 40th Int. Conf. Machine Learning, Honolulu, HI, USA, 2023, pp. 414.
    [100]
    X. Ma, G. Fang, and X. Wang, “LLM-pruner: On the structural pruning of large language models,” in Proc. 37th Int. Conf. Neural Information Processing Systems, New Orleans, LA, USA, 2023, pp. 950.
    [101]
    Z. Liu, B. Oguz, C. Zhao, E. Chang, P. Stock, Y. Mehdad, Y. Shi, R. Krishnamoorthi, and V. Chandra, “LLM-QAT: Data-free quantization aware training for large language models,” in Proc. Findings of the Association for Computational Linguistics, Bangkok, Thailand, 2024, pp. 467–484.
    [102]
    Y. Li, Y. Yu, C. Liang, N. Karampatziakis, P. He, W. Chen, and T. Zhao, “LoftQ: LoRA-fine-tuning-aware quantization for large language models,” in Proc. 12th Int. Conf. Learning Representations, Vienna, Austria, 2024.
    [103]
    Y. Wang, Y. Wang, J. Cai, T. K. Lee, C. Miao, and Z. J. Wang, “SSD-KD: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images,” Med. Image Anal., vol. 84, p. 102693, 2023. doi: 10.1016/j.media.2022.102693
    [104]
    N. Ho, L. Schmid, and S.-Y. Yun, “Large language models are reasoning teachers,” in Proc. 61st Annu. Meeting of the Association for Computational Linguistics, Toronto, Canada, 2023, pp. 14852–14882.
    [105]
    X. Ma, S. Jeong, M. Zhang, D. Wang, J. Choi, and M. Jeon, “Cost-effective on-device continual learning over memory hierarchy with Miro,” in Proc. 29th Annu. Int. Conf. Mobile Computing and Networking, New York, NY, USA, 2023, pp. 83.
    [106]
    Y. Nan, S. Jiang, and M. Li, “Large-scale video analytics with cloud–edge collaborative continuous learning,” ACM Trans. Sensor Netw., vol. 20, no. 1, p. 14, Oct. 2023.
    [107]
    H. Daga, Y. Chen, A. Agrawal, and A. Gavrilovska, “CLUE: Systems support for knowledge transfer in collaborative learning with neural nets,” IEEE Trans. Cloud Comput., vol. 11, no. 4, pp. 3541–3554, Oct.-Dec. 2023. doi: 10.1109/TCC.2023.3294490
    [108]
    Y. Dong, X. Jiang, Z. Jin, and G. Li, “Self-collaboration code generation via chatGPT,” ACM Trans. Softw. Eng. Methodol., vol. 33, no. 7, p. 189, 2024.
    [109]
    G. Li, H. A. Al Kader Hammoud, H. Itani, D. Khizbullin, and B. Ghanem, “CAMEL: Communicative agents for “mind” exploration of large language model society,” in Proc. 37th Int. Conf. Neural Information Processing Systems, New Orleans, LA, USA, 2023, pp. 2264.
    [110]
    C. Qian, W. Liu, H. Liu, N. Chen, Y. Dang, J. Li, C. Yang, W. Chen, Y. Su, X. Cong, J. Xu, D. Li, Z. Liu, and M. Sun, “ChatDev: Communicative agents for software development,” in Proc. 62nd Annu. Meeting of the Association for Computational Linguistics, Bangkok, Thailand, 2024, pp. 15174–15186.
    [111]
    Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, L. Jiang, X. Zhang, S. Zhang, J. Liu, A. H. Awadallah, R. W. White, D. Burger, and C. Wang, “AutoGen: Enabling next-gen LLM applications via multi-agent conversation,” arXiv preprint arXiv: 2308.08155, 2023.
    [112]
    J. S. Park, J. O$’$Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, “Generative agents: Interactive simulacra of human behavior,” in Proc. 36th Annu. ACM Symp. on User Interface Software and Technology, San Francisco, CA, USA, 2023, p. 2.
    [113]
    Z. Wang, S. Mao, W. Wu, T. Ge, F. Wei, and H. Ji, “Unleashing the emergent cognitive synergy in large language models: A task-solving agent through multi-persona self-collaboration,” in Proc. Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Mexico City, Mexico, 2024, pp. 257–279.
    [114]
    C.-M. Chan, W. Chen, Y. Su, J. Yu, W. Xue, S. Zhang, J. Fu, and Z. Liu, “ChatEval: Towards better LLM-based evaluators through multi-agent debate,” in Proc. 12th Int. Conf. Learning Representations, Vienna, Austria, 2024.
    [115]
    D. Jiang, X. Ren, and B. Y. Lin, “LLM-blender: Ensembling large language models with pairwise ranking and generative fusion,” in Proc. 61st Annu. Meeting of the Association for Computational Linguistics, Toronto, Canada, 2023, pp. 14165–14178.
    [116]
    Z. Liu, Y. Zhang, P. Li, Y. Liu, and D. Yang, “Dynamic LLM-agent network: An LLM-agent collaboration framework with agent team optimization,” arXiv preprint arXiv: 2310.02170, 2024.
    [117]
    W. Chen, Y. Su, J. Zuo, C. Yang, C. Yuan, C.-M. Chan, H. Yu, Y. Lu, Y.-H. Hung, C. Qian, Y. Qin, X. Cong, R. Xie, Z. Liu, M. Sun, and J. Zhou, “AgentVerse: Facilitating multi-agent collaboration and exploring emergent behaviors,” in Proc. 12th Int. Conf. on Learning Representations, Vienna, Austria, 2024.
    [118]
    M. Zhuge, W. Wang, L. Kirsch, F. Faccio, D. Khizbullin, and J. Schmidhuber, “Language agents as optimizable graphs,” arXiv preprint arXiv: 2402.16823, 2024.
    [119]
    H. Zhou, T. He, Y.-S. Ong, G. Cong, and Q. Chen, “Differentiable clustering for graph attention,” IEEE Trans. Knowl. Data Eng., vol. 36, no. 8, pp. 3751–3764, Aug. 2024. doi: 10.1109/TKDE.2024.3363703
    [120]
    M. Chen, X. Wu, X. Tang, T. He, Y.-S. Ong, Q. Liu, Q. Lao, and H. Yu, “Free-rider and conflict aware collaboration formation for cross-silo federated learning,” arXiv preprint arXiv: 2410.19321, 2024.
    [121]
    Y. Liu, P. Huang, F. Yang, K. Huang, and L. Shu, “QuAsyncFL: Asynchronous federated learning with quantization for cloud-edge-terminal collaboration enabled AIoT,” IEEE Internet Things J., vol. 11, no. 1, pp. 59–69, Jan. 2024. doi: 10.1109/JIOT.2023.3290818
    [122]
    X. Wang, C. Wang, X. Li, V. C. M. Leung, and T. Taleb, “Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching,” IEEE Internet Things J., vol. 7, no. 10, pp. 9441–9455, Oct. 2020. doi: 10.1109/JIOT.2020.2986803
    [123]
    Y. Mao, J. Zhang, and K. B. Letaief, “Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems,” in Proc. IEEE Wireless Communications and Networking Conf., San Francisco, CA, USA, 2017, pp. 1–6.
    [124]
    Y. Tian, X. Yang, J. Zhang, Y. Dong, and H. Su, “Evil geniuses: Delving into the safety of LLM-based agents,” arXiv preprint arXiv: 2311.11855, 2024.
    [125]
    P. Manakul, A. Liusie, and M. J. F. Gales, “SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models,” in Proc. Conf. Empirical Methods in Natural Language Processing, Singapore, Singapore, 2023, pp. 9004–9017.
    [126]
    T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50–60, May 2020. doi: 10.1109/MSP.2020.2975749
    [127]
    C. Niu, F. Wu, S. Tang, L. Hua, R. Jia, C. Lv, Z. Wu, and G. Chen, “Billion-scale federated learning on mobile clients: A submodel design with tunable privacy,” in Proc. 26th Annu. Int. Conf. Mobile Computing and Networking, London, UK, 2020, pp. 31.
    [128]
    X. Ge, Q.-L. Han, Q. Wu, and X.-M. Zhang, “Resilient and safe platooning control of connected automated vehicles against intermittent denial-of-service attacks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1234–1251, May 2023. doi: 10.1109/JAS.2022.105845
    [129]
    L. Wang, Z. Wang, Q.-L. Han, and G. Wei, “Synchronization control for a class of discrete-time dynamical networks with packet dropouts: A coding-decoding-based approach,” IEEE Trans. Cybern., vol. 48, no. 8, pp. 2437–2448, Aug. 2018. doi: 10.1109/TCYB.2017.2740309
    [130]
    D. Ding, Z. Wang, and Q.-L. Han, “Neural-network-based consensus control for multiagent systems with input constraints: The event-triggered case,” IEEE Trans. Cybern., vol. 50, no. 8, pp. 3719–3730, Aug. 2020. doi: 10.1109/TCYB.2019.2927471
    [131]
    J. Blumenkamp, A. Shankar, M. Bettini, J. Bird, and A. Prorok, “The cambridge robomaster: An agile multi-robot research platform,” arXiv preprint arXiv: 2405.02198, 2024.
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