Toward the next frontier of embodied AI

Abstract

Embodied artificial intelligence has emerged as a transformative paradigm, marking a fundamental shift in artificial intelligence research toward systems that tightly couple perception, cognition, and action within real-world environments. This editorial emphasizes the growing significance of embodied artificial intelligence, introduces the key contributions presented in this Special Issue, and provides an overview of the current challenges and prospective research directions shaping the future of the field.

Keywords

Robotics / artificial intelligence / embodied AI

References

[1] Li J.Digital twins to embodied artificial intelligence: review and perspective.Intell Robot2025;5:202-27

[2] Chen W,Ji S.A survey of autonomous robots and multi-robot navigation: perception, planning and collaboration.Biomim Intell Robot2025;5:100203

[3] Bin T,Wang N,Yao J.A survey on the visual perception of humanoid robot.Biomim Intell Robot2025;5:100197

[4] Fan, R., Guo, S., Bocus, M. J. Autonomous driving perception. Cham, Switzerland: Springer, 2023. https://scholar.google.com/scholar?q=Autonomous+driving+perception.+Cham,+Switzerland:+Springer+2023&hl=zh-CN&as_sdt=0&as_vis=1&oi=scholart (accessed 2025-11-24).

[5] Huang Y,Duan H.Human-like dexterous manipulation for anthropomorphic five-fingered hands: a review.Biomim Intell Robot2025;5:100212

[6] Mon-Williams R,Long R,Lucas CG.Embodied large language models enable robots to complete complex tasks in unpredictable environments.Nat Mach Intell2025;7:592-601 PMCID:PMC12088599

[7] Liu Z,Jia N,Yang J.SANet: scale-adaptive network for lightweight salient object detection.Intell Robot2024;4:503-23

[8] He H,Li Y.MSAFNet: a novel approach to facial expression recognition in embodied AI systems.Intell Robot2025;5:313-32

[9] Zhuang T,Xue B.An in-vehicle real-time infrared object detection system based on deep learning with resource-constrained hardware.Intell Robot2024;4:276-92

[10] Zhang C,Li J,Mao Z.Large language models for human-robot interaction: a review.Biomim Intell Robot2023;3:100131

[11] Liu H,Hou C.Parallel implementation for real-time visual SLAM systems based on heterogeneous computing.Intell Robot2024;4:256-75

[12] Chen, Q.; Wang, C.; Wang, D.; Zhang, T.; Li, W.; He, X. Lifelong knowledge editing for vision language models with low-rank mixture-of-experts. arXiv 2004, arXiv:2411.15432.

[13] Huang J,Jia N.RoadFormer+: delivering RGB-X scene parsing through scale-aware information decoupling and advanced heterogeneous feature fusion.IEEE Trans Intell Veh2025;10:3156-65

[14] Liu, Y.; Chen, Q.; Albanie, S. Adaptive cross-modal prototypes for cross-domain visual-language retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2021. pp 14954-64. https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Adaptive_Cross (accessed 2025-11-24).

[15] Guo S,Wu Z,Pitas I.LIX: implicitly infusing spatial geometric prior knowledge into visual semantic segmentation for autonomous driving.IEEE Trans Image Process2025;34:7250-63

[16] Liu, C., Chen, Q., Fan, R. Playing to vision foundation model’s strengths in stereo matching. IEEE, 2024; pp 1-12.

[17] Liu H,Xie H.Lightweight deep learning for resource-constrained environments: a survey.ACM Comput Surv2024;56:1-42