Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)

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Review of Multimodal Data Fusion for Robotics and Embodied Intelligence

Authors
Yukai Qi1, *
1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, 430000, China
*Corresponding author. Email: u202210623@hust.edu.cn
Corresponding Author
Yukai Qi
Available Online 23 October 2025.
DOI
10.2991/978-94-6463-864-6_47How to use a DOI?
Keywords
Multimodal Data Fusion; Sensors; Deep Learning; Embodied Intelligence; Robotics
Abstract

Multimodal data fusion for embodied intelligence enable physical intelligent systems to perceive, reason, and interact with their environment by integrating heterogeneous perceptual data. Recent years have witnessed significant progress made in embodied intelligence systems, driven by the substantial growth of high-quality multimodal data collected by various sensors and the rapid development of deep learning techniques. This paper reviews the latest methods in multimodal data fusion, focusing on hardware integration, perception algorithms, and emerging deep learning paradigms that have significantly advanced the efficiency and intelligence of robots in processing process multisensory information. Furthermore, this paper discusses the main challenges faced by embodied multimodal fusion, such as real-time processing constraints, robustness in dynamic environments, and data generalizability, then proposes potential research directions. This comprehensive review aims to provide researchers and practitioners insights into the current landscape and future prospects of multimodal fusion for embodied intelligent systems by systematically organizing current progress and challenges.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
Series
Advances in Engineering Research
Publication Date
23 October 2025
ISBN
978-94-6463-864-6
ISSN
2352-5401
DOI
10.2991/978-94-6463-864-6_47How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yukai Qi
PY  - 2025
DA  - 2025/10/23
TI  - Review of Multimodal Data Fusion for Robotics and Embodied Intelligence
BT  - Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
PB  - Atlantis Press
SP  - 514
EP  - 529
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-864-6_47
DO  - 10.2991/978-94-6463-864-6_47
ID  - Qi2025
ER  -