Skip to main content
SpringerLink
Log in

The Role of Artificial Intelligence and Robotic Solution Technologies in Metaverse Design

  • Chapter
  • First Online:
Metaverse

Part of the book series: Studies in Big Data ((SBD,volume 133))

  • 788 Accesses

Abstract

Metaverse is designed as a time and space-independent environment where virtual and reality will be intertwined, real-time and multi-user, human–computer–robot interactions will be possible. Metaverse design needs to be handled together with the internet, social networks, computer games, virtual reality glasses, augmented reality software, the internet of things, wearable devices, 5G/6G internet infrastructure, cryptocurrencies, artificial intelligence, and robotics technologies. The Metaverse in design is a concept that covers every field, from education to art, from commerce to health, and from games to entertainment. Platforms, where many people of different languages, religions, races, and ages can interact simultaneously with avatars, AR/VR, smart devices, and wearables, have the potential to generate enormous amounts of data. The analysis of this produced data with advanced artificial intelligence techniques has critical importance in terms of both the ecosystem’s continuity and the user experience’s improvement. Artificial intelligence and robotic technologies act as a bridge between the real and virtual worlds in Metaverse platforms. To provide communication between human–avatar, avatar–avatar, human–robot, and robot–avatar, natural language recognition, voice recognition, voice-to-text conversion, and text-to-speech conversion tasks can be performed with artificial intelligence technologies. Metaverse platforms, where individuals in the real world can control their avatars or robots in the virtual world with various hardware, communicate with others, and produce rich digital content, also promise to make their economies. However, such a Metaverse ecosystem has not been created, as the necessary technology and infrastructure are not yet available. In addition, the hardware required for users to enter these Metaverse platforms is limited in number, quite expensive, cumbersome, and unsuitable for long-term use. For this reason, technology companies and social media giants are investing heavily to create their Metaverse ecosystems. Therefore, the future of the internet is shaped in parallel with the development of infrastructure and technology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gupta, A., Hellebrekers, T.: ReSkin: a versatile, replaceable, low-cost skin for AI research on tactile perception. Facebook. https://ai.facebook.com/blog/reskin-a-versatile-replaceable-low-cost-skin-for-ai-research-on-tactile-perception. Accessed 20 May 2022

  2. Novel AR-based smart contact lenses can be used as computer screen. https://www.gadgetsnow.com/tech-news/novel-ar-based-smart-contact-lenses-can-be-used-as-computer-screen/articleshow/91693766.cms. Accessed 20 May 2022

  3. Daniluk, M., Rocktäschel, T., Welbl, J., Riedel, S.: Frustratingly short attention spans in neural language modeling (2017). https://arxiv.org/abs/1702.04521. Accessed 20 May 2022

  4. Beneš, K., Baskar, M.K., Burget, L.: Residual memory networks in language modeling: improving the reputation of feed-forward networks. In: Interspeech 2017, Stockholm, Sweden (2017)

    Google Scholar 

  5. Athiwaratkun, B., Stokes, J.W.: Malware classification with LSTM and GRU language models and a character-level CNN. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2482–2486. IEEE Press, New Orleans (2017)

    Google Scholar 

  6. Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep Convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)

    Article  Google Scholar 

  7. Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing [Review article]. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)

    Article  Google Scholar 

  8. Wu, P., Ding, W., You, Z., An, P.: Virtual reality video quality assessment based on 3d Convolutional neural networks. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 3187–3191. IEEE Press. Taipei (2019)

    Google Scholar 

  9. Liu, D., Fu, J., Qu, Q., Lv, J.: BFGAN: Backward and forward generative adversarial networks for lexically constrained sentence generation. IEEE/ACM Trans. Audio Speech Lang. Process. 27(12), 2350–2361 (2019)

    Article  Google Scholar 

  10. Berner, C., Brockman, G., Chan, B., Cheung, V., Debiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., Józefowicz, R., Gray, S., Olsson, C., Pachocki, J.W., Petrov, M., Pinto, H.P., Raiman, J., Salimans, T., Schlatter, J., Schneider, J., Sidor, S., Sutskever, I., Tang, J., Wolski, F., Zhang, S.: Dota 2 with large scale deep reinforcement learning. https://arxiv.org/abs/1912.06680. Accessed 20 May 2022

  11. Jin, N., Wu, J., Ma, X., Yan, K., Mo, Y.: Multi-task learning model based on multi-scale CNN and LSTM for sentiment classification. IEEE Access 8, 77060–77072 (2020)

    Article  Google Scholar 

  12. Hu, Z., Bulling, A., Li, S., Wang, G.: FixationNet: forecasting eye fixations in task-oriented virtual environments. IEEE Trans. Vis. Comput. Graph. 27(5), 2681–2690 (2021)

    Article  Google Scholar 

  13. Jin, Y., Chen, M., Goodall, T., Patney, A., Bovik, A.C.: Subjective and objective quality assessment of 2D and 3D Foveated video compression in virtual reality. IEEE Trans. Image Process. 30, 5905–5919 (2021)

    Article  Google Scholar 

  14. Lee, K., Sengupta, S.: Introducing the AI research supercluster—meta’s cutting-edge AI supercomputer for AI research. Facebook. https://ai.facebook.com/blog/ai-rsc/. Accessed 20 May 2022

  15. Dong, R., She, C., Hardjawana, W., Li, Y., Vucetic, B.: Deep learning for hybrid 5G services in mobile edge computing systems: learn from a digital twin. IEEE Trans. Wirel. Commun. 18(10), 4692–4707 (2019)

    Article  Google Scholar 

  16. Luo, C., Ji, J., Wang, Q., Chen, X., Li, P.: Channel state information prediction for 5G wireless communications: a deep learning approach. IEEE Trans. Netw. Sci. Eng. 7(1), 227–236 (2020)

    Article  MathSciNet  Google Scholar 

  17. Wang, S., Sun, S., Wang, X., Ning, Z., Rodrigues, J.J.: Secure crowdsensing in 5G Internet of vehicles: When deep reinforcement learning meets blockchain. IEEE Consum. Electron. Mag. 10(5), 72–81 (2020)

    Article  Google Scholar 

  18. Oughton, E.J., Lehr, W., Katsaros, K., Selinis, I., Bubley, D., Kusuma, J.: Revisiting wireless internet connectivity: 5G vs Wi-Fi 6. Telecommun. Policy 45(5), 102127 (2021)

    Article  Google Scholar 

  19. Alsenwi, M., Tran, N.H., Bennis, M., Pandey, S.R., Bairagi, A.K., Hong, C.S.: Intelligent resource slicing for eMBB and URLLC coexistence in 5G and beyond: a deep reinforcement learning based approach. IEEE Trans. Wirel. Commun. 20(7), 4585–4600 (2021)

    Article  Google Scholar 

  20. LeCun,Y., Zuckerberg, M. ve Fergus R.: Meta AI. https://ai.facebook.com/. Accessed 20 May 2022

  21. PyTorch3D. A library for deep learning with 3D data. https://pytorch3d.org/. Accessed 20 May 2022

  22. NVIDIA TensorRT. NVIDIA developer. https://developer.nvidia.com/tensorrt. Accessed 20 May 2022

  23. Cachada, A.: Nvidia GANverse3D–2D Photo to a 3D model with texture at a click of a button!|Spltech Smart Solutions. https://spltech.co.uk/nvidia-ganverse3d-2d-photo-to-a-3d-model-with-texture-at-a-click-of-a-button/. Accessed 20 May 2022

  24. Dean, T.L., Dean, T., Allen, J., Aloimonos, J., Aloimonos, Y.: Artificial Intelligence: Theory and Practice. Addison-Wesley (1995)

    Google Scholar 

  25. Andreu-Perez, J., Deligianni, F., Ravì, D., Yang, G.: Artificial intelligence and robotics. https://arxiv.org/abs/1803.10813. Accessed 20 May 2022

  26. Ning, H., Wang, H., Lin, Y., Wang, W., Dhelim, S., Farha, F., Ding, J., Daneshmand, M.: A Survey on Metaverse: The State-of-the-Art. Technologies, Applications, and Challenges. https://arxiv.org/abs/2111.09673

  27. Radoff, J.: The metaverse value-chain—building the metaverse. Medium, https://medium.com/building-the-Metaverse/the-Metaverse-value-chain-afcf9e09e3a7. Accessed 20 May 2022

  28. Bonaccorso, G.: Machine Learning Algorithms. Packt Publishing (2017)

    Google Scholar 

  29. Maimon, O., Rokach, L.: Data Mining and Knowledge Discovery Handbook. Springer Science & Business Media (2005)

    Google Scholar 

  30. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997)

    Article  MATH  Google Scholar 

  31. Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)

    Article  Google Scholar 

  32. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    Article  MATH  Google Scholar 

  33. Bishop, C.M.: Neural networks and their applications. Rev. Sci. Instrum. 65(6), 1803–1832 (1994)

    Article  Google Scholar 

  34. Mikolov, T., Karafiát, M., Burget, L., Černocký, J., Khudanpur, S.: Recurrent neural network based language model. In: Interspeech 2010, Makuhari, Chiba, pp.1045–1048 (2010)

    Google Scholar 

  35. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, vol. 3361, no. 10 (1995)

    Google Scholar 

  36. Kohonen, T.: The self-organizing map. Inst. Electr. Electron. Eng. 78(9), 1464–1480. IEEE Press (1990)

    Google Scholar 

  37. Lange, S., Riedmiller, M.A.: Deep auto-encoder neural networks in reinforcement learning. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp.1–8. IEEE Press, Barcelona (2010)

    Google Scholar 

  38. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)

    Google Scholar 

  39. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  40. Chung, J., Gülçehre, Ç., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. https://arxiv.org/abs/1412.3555. Accessed 20 May 2022

  41. Lin, M., Chen, Q., Yan, S.: Network in network. https://arxiv.org/abs/1312.4400. Accessed 20 May 2022

  42. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. https://arxiv.org/abs/1409.1556. Accessed 20 May 2022

  43. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. https://arxiv.org/abs/1409.4842. Accessed 20 May 2022

  44. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. https://arxiv.org/abs/1512.03385. Accessed 20 May 2022

  45. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  46. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4700–4708 (2017)

    Google Scholar 

  47. Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. https://arxiv.org/abs/1905.11946. Accessed 20 May 2022

  48. Huynh-The, T., Pham, Q., Pham, X., Nguyen, T.T., Han, Z., Kim, D.: Artificial intelligence for the metaverse: a survey. https://arxiv.org/abs/2202.10336. Accessed 20 May 2022

  49. Lahiri, A., Bairagya, S., Bera, S., Haldar, S., Biswas, P.K.: Lightweight modules for efficient deep learning based image restoration. IEEE Trans. Circuits Syst. Video Technol. 31(4), 1395–1410 (2021)

    Article  Google Scholar 

  50. Dang, Q., Yin, J., Wang, B., Zheng, W.: Deep learning based 2D human pose estimation: a survey. Tsinghua Sci. Technol. 24(6), 663–676 (2019)

    Article  Google Scholar 

  51. Gadekallu, T.R., Pham, Q.V., Nguyen, D.C., Maddikunta, P.K.R., Deepa, N., Prabadevi, B., Pathirana, P.N., Zhao, J., Hwang, W.J.: Blockchain for edge of things: applications, opportunities, and challenges. IEEE Internet Things J. 9(2), 964–988 (2022)

    Article  Google Scholar 

  52. Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., Luo, W.: DeepChain: auditable and privacy-preserving deep learning with blockchain-based incentive. In: IEEE Transactions on Dependable and Secure Computing, pp. 2438–2455. IEEE Press (2019)

    Google Scholar 

  53. Nguyen, D.C., Pathirana, P.N., Ding, M., Seneviratne, A.: Privacy-preserved task offloading in mobile blockchain with deep reinforcement learning. IEEE Trans. Netw. Serv. Manage. 17(4), 2536–2549 (2020)

    Article  Google Scholar 

  54. She, C., Dong, R., Gu, Z., Hou, Z., Li, Y., Hardjawana, W., Yang, C., Song, L., Vucetic, B.: Deep learning for ultra-reliable and low-latency communications in 6G networks. IEEE Netw. 34(5), 219–225 (2020)

    Article  Google Scholar 

  55. Gu, B., Zhang, X., Lin, Z., Alazab, M.: Deep multiagent reinforcement-learning-based resource allocation for Internet of controllable things. IEEE Internet Things J. 8(5), 3066–3074 (2021)

    Article  Google Scholar 

  56. Rathore, M.M., Shah, S.A., Shukla, D., Bentafat, E., Bakiras, S.: The role of AI, machine learning, and big data in digital twinning: a systematic literature review, challenges, and opportunities. IEEE Access 9, 32030–32052 (2021)

    Article  Google Scholar 

  57. Darvishi, H., Ciuonzo, D., Eide, E.R., Rossi, P.S.: Sensor-fault detection, isolation and accommodation for digital twins via modular data-driven architecture. IEEE Sens. J. 21(4), 4827–4838 (2021)

    Article  Google Scholar 

  58. Wang, Q., Jiao, W., Wang, P., Zhang, Y.: Digital twin for human-robot interactive welding and welder behavior analysis. IEEE/CAA J. Automatica Sinica 8(2), 334–343 (2020)

    Article  Google Scholar 

  59. Elayan, H., Aloqaily, M., Guizani, M.: Digital twin for intelligent context-aware IoT healthcare systems. IEEE Internet Things J. 8(23), 16749–16757 (2021)

    Article  Google Scholar 

  60. Matran-Fernandez, A., Poli, R.: Brain–computer interfaces for detection and localization of targets in aerial images. IEEE Trans. Biomed. Eng. 64(4), 959–969 (2017)

    Article  Google Scholar 

  61. Lv, Z., Qiao, L., Wang, Q., Piccialli, F.: Advanced machine-learning methods for brain-computer interfacing. IEEE/ACM Trans. Comput. Biol. Bioinf. 18, 1688–1698 (2021)

    Article  Google Scholar 

  62. Liu, B., Yin, G.: Chinese document classification with Bi-directional Convolutional language model. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1785–1788. Association for Computing Machinery, New York (2020)

    Google Scholar 

  63. Sharma, R., Morwal, S., Agarwal, B., Chandra, R., Khan, M.S.: A deep neural network-based model for named entity recognition for Hindi language. Neural Comput. Appl. 32(20), 16191–16203 (2020)

    Article  Google Scholar 

  64. Lai, K., Lin, C., Kang, C., Liao, M., Chen, M.: VIVID: Virtual environment for visual deep learning. In: Proceedings of the 26th ACM international conference on Multimedia, pp.1356–1359. Association for Computing Machinery, New York (2018)

    Google Scholar 

  65. Lee, J., Lee, K.H.: Precomputing avatar behavior from human motion data. Graph. Models 68(2), 158–174 (2006)

    Article  Google Scholar 

  66. Wang,H. Gao, Y., Chen., X.: RL-DOT: a reinforcement learning NPC team for playing domination games. IEEE Trans. Comput. Intell. AI Games 2(1), 17–26 (2010)

    Google Scholar 

  67. Rahmatizadeh, R., Abolghasemi, P., Behal, A., Bölöni, L.: From virtual demonstration to real-world manipulation using LSTM and MDN. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)

    Google Scholar 

  68. Wang, J., Hu, Y.: An improved enhancement algorithm based on CNN applicable for weak contrast images. IEEE Access 8, 8459–8476 (2020)

    Article  Google Scholar 

  69. Mei, S., Jiang, R., Li, X., Du, Q.: Spatial and spectral joint super-resolution using convolutional neural network. IEEE Trans. Geosci. Remote Sens. 58, 4590–4603 (2020)

    Article  Google Scholar 

  70. Chen, K., Gong, S., Xiang, T.: Human pose estimation using structural support vector machines. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 846–851. IEEE Press (2011)

    Google Scholar 

  71. Rogez, G., Weinzaepfel, P., Schmid, C.: LCR-net++: multi-person 2D and 3D pose detection in natural images. IEEE Trans. Pattern Anal. Mach. Intell. https://arxiv.org/abs/1803.00455. Accessed 20 May 2022

  72. Huynh-The, T., Hua, C., Tu, N.A., Hur, T.H., Bang, J.H., Kim, D., Amin, M.B., Kang, B.H., Seung, H., Shin, S., Kim, E., Lee, S.: Hierarchical topic modeling with pose-transition feature for action recognition using 3D skeleton data. Inf. Sci. 444, 20–35 (2018)

    Article  MathSciNet  Google Scholar 

  73. Huynh-The, T., Hua, C., Kim, D.: Encoding pose features to images with data augmentation for 3-D action recognition. IEEE Trans. Industr. Inf. 16(5), 3100–3111 (2019)

    Article  Google Scholar 

  74. Tanwar, S., Bhatia, Q., Patel, P., Kumari, A., Singh, P.K., Hong, W.: Machine learning adoption in blockchain-based smart applications: the challenges, and a way forward. IEEE Access 8, 474–488 (2020)

    Article  Google Scholar 

  75. Khan, M.A., Abbas, S., Rehman, A., Saeed, Y., Zeb, A., Uddin, M.I., Nasser, N., Ali, A.: A machine learning approach for blockchain-based smart home networks security. IEEE Netw. 35(3), 223–229 (2021)

    Article  Google Scholar 

  76. Fan, S., Zhang, H., Zeng, Y., Cai, W.: Hybrid blockchain-based resource trading system for federated learning in edge computing. IEEE Internet Things J. 8(4), 2252–2264 (2021)

    Article  Google Scholar 

  77. Liu, H., Zhang, S., Zhang, P., Zhou, X., Shao, X., Pu, G., Zhang, Y.: Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Trans. Veh. Technol. 70(6), 6073–6084 (2021)

    Article  Google Scholar 

  78. Azari, A., Ozger, M., Cavdar, C.: Risk-aware resource allocation for URLLC: challenges and strategies with machine learning. IEEE Commun. Mag. 57(3), 42–48 (2019)

    Article  Google Scholar 

  79. Guo, S., Lin, Y., Li, S., Chen, Z., Wan, H.: Deep spatial–temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans. Intell. Transp. Syst. 20(10), 3913–3926 (2019)

    Article  Google Scholar 

  80. Ghandar, A., Ahmed, A., Zulfiqar, S., Hua, Z., Hanai, M., Theodoropoulos, G.: A decision support system for urban agriculture using digital twin: a case study with Aquaponics. IEEE Access 9, 35691–35708 (2021)

    Article  Google Scholar 

  81. Xu, X., Shen, B., Ding, S., Srivastava, G., Bilal, M., Khosravi, M.R., Menon, V.G., Jan, M.A., Wang, M.: Service offloading with deep Q-network for digital twinning-empowered Internet of vehicles in edge computing. IEEE Trans. Indus. Inf. 18(2), 1414–1423 (2022)

    Article  Google Scholar 

  82. Song, Q., Lei, S., Sun, W., Zhang, Y.: Adaptive federated learning for digital twin driven industrial Internet of Things. In: 2021 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE Press, Nanjing, China (2021)

    Google Scholar 

  83. Abibullaev, B., Zollanvari, A.: Learning discriminative Spatiospectral features of ERPs for accurate brain–computer interfaces. IEEE J. Biomed. Health Inform. 23(5), 2009–2020 (2019)

    Article  Google Scholar 

  84. Jeong, J., Shim, K., Kim, D., Lee, S.: Brain-controlled robotic arm system based on multi-directional CNN-bilstm network using EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 28(5), 1226–1238 (2020)

    Article  Google Scholar 

  85. Santamaría-Vázquez, E., Martínez-Cagigal, V., Vaquerizo-Villar, F., Hornero, R.: EEG-inception: a novel deep convolutional neural network for assistive ERP-based brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 2773–2782 (2020)

    Article  Google Scholar 

  86. Felicity, T.: Meta plans to make robotic eyeball that can track human eye movements for the metaverse. Tech Times. https://www.techtimes.com/articles/270706/20220118/meta-plans-to-make-robotic-eyeball-that-can-track-human-eye-movements-for-the-Metaverse.htm. Accessed 4 Aug 2022

  87. Welcome Back to CES 2022. CES. https://videos.ces.tech/. Accessed 5 Aug 2022

  88. Hyundai Motor Shares Vision of New Metamobility Concept. Expanding Human Reach’ through Robotics & Metaverse at CES 2022, Hyundai Motors. https://www.hyundai.com/worldwide/en/company/newsroom/hyundai-motor-shares-vision-of-new-metamobility-concept,-%E2%80%98expanding-human-reach%E2%80%99-through-robotics-&-Metaverse-at-ces-2022-0000016777. Accessed 5 Aug 2022

  89. Beyond Imagination. https://www.beomni.ai/. Accessed 5 Aug 2022

  90. Ameca. Engineered arts. https://www.engineeredarts.co.uk/robot/ameca/. Accessed 5 Aug 2022

  91. OWO—Feel the game, OWO. https://owogame.com/. Accessed 5 Aug 2022

  92. BMW at CES 2022. The BMW iX Flow featuring E Ink. https://www.bmw.com/en/events/ces2022/ixflow.html. Accessed 5 Aug 2022

  93. Moline, I.: John Deere reveals fully autonomous tractor at CES 2022. https://www.deere.com/en/news/all-news/autonomous-tractor-reveal/. Accessed 5 Aug 2022

  94. Sengled. https://us.sengled.com/. Accessed 5 Aug 2022

  95. Kastanis, I., Slater, M.: Reinforcement learning utilizes proxemics. ACM Trans. Appl. Percept. 9(1), 1–15 (2012)

    Article  Google Scholar 

  96. Ma, R., Yu, T., Zhong, X., Yu, Z.L., Li, Y., Gu, Z.: Capsule network for ERP detection in brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 718–730 (2021)

    Article  Google Scholar 

  97. Chen, D., Xie, L. J., Kim, B., Wang, L., Hong, C. S., Wang, L., Han, Z.: Federated learning based mobile edge computing for augmented reality applications. In: 2020 International Conference on Computing, Networking and Communications (ICNC), pp.767–773. IEEE Press, Big Island (2020)

    Google Scholar 

  98. Mohammadi, M., Al-Fuqaha, A.: Enabling cognitive smart cities using big data and machine learning: approaches and challenges. IEEE Commun. Mag. 56(2), 94–101 (2018)

    Article  Google Scholar 

  99. Yampolskiy, R.V., Klare, B., Jain, A. K.: Face recognition in the virtual world: recognizing avatar faces. In: Proceedings of SPIE, pp. 40–45. IEEE Press, Boca Raton (2012)

    Google Scholar 

  100. Fabbri, M., Lanzi, F., Calderara, S., Palazzi, A., Vezzani, R., Cucchiara, R.: Learning to detect and track visible and occluded body joints in a virtual world. In: Computer Vision—ECCV 2018, pp. 450–466 (2018)

    Google Scholar 

  101. Chen, J.F., Lin, W.C., Bai, H.S., Yang, C.C., Chao, H.C.: Constructing an intelligent behavior avatar in a virtual world: A self-learning model based on reinforcement. In: IRI—2005 IEEE International Conference on Information Reuse and Integration, pp. 421–426. IEEE Press, Las Vegas (2005)

    Google Scholar 

  102. Wang, T., Li, J., Deng, Y., Wang, C., Snoussi, H., Tao, F.: Digital twin for human-machine interaction with convolutional neural network. Int. J. Comput. Integr. Manuf. 34, 888–897 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mugla Sitki Kocman University, Mugla, Türkiye

    Nida Gokce Narin

Authors
  1. Nida Gokce Narin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nida Gokce Narin .

Editor information

Editors and Affiliations

  1. Ankara University, Ankara, Türkiye

    Fatih Sinan Esen

  2. Endicott College of International Studies, Woosong University, Daejeon, Korea (Republic of)

    Hasan Tinmaz

  3. College of Engineering Technology Management (ETM), Oregon Institute of Technology (Oregon Tech), Oregon, OR, USA

    Madhusudan Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gokce Narin, N. (2023). The Role of Artificial Intelligence and Robotic Solution Technologies in Metaverse Design. In: Esen, F.S., Tinmaz, H., Singh, M. (eds) Metaverse. Studies in Big Data, vol 133. Springer, Singapore. https://doi.org/10.1007/978-981-99-4641-9_4

Download citation

Publish with us

Policies and ethics

Search

Navigation