Agentic AI-Empowered Conversational Embodied Intelligence Networks in 6G
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908673606942720 |
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| author | Chen, Mingkai Feng, Zijie Wang, Lei Khamayseh, Yaser |
| author_facet | Chen, Mingkai Feng, Zijie Wang, Lei Khamayseh, Yaser |
| contents | In the 6G era, semantic collaboration among multiple embodied intelligent devices (MEIDs) becomes crucial for complex task execution. However, existing systems face challenges in multimodal information fusion, adaptive communication, and decision interpretability. To address these limitations, we propose a collaborative Conversational Embodied Intelligence Network (CC-EIN) integrating multimodal feature fusion, adaptive semantic communication, task coordination, and interpretability. PerceptiNet performs cross-modal fusion of image and radar data to generate unified semantic representations. An adaptive semantic communication strategy dynamically adjusts coding schemes and transmission power according to task urgency and channel quality. A semantic-driven collaboration mechanism further supports task decomposition and conflict-free coordination among heterogeneous devices. Finally, the InDec module enhances decision transparency through Grad-CAM visualization. Simulation results in post-earthquake rescue scenarios demonstrate that CC-EIN achieves 95.4% task completion rate and 95% transmission efficiency while maintaining strong semantic consistency and energy efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic AI-Empowered Conversational Embodied Intelligence Networks in 6G Chen, Mingkai Feng, Zijie Wang, Lei Khamayseh, Yaser Artificial Intelligence In the 6G era, semantic collaboration among multiple embodied intelligent devices (MEIDs) becomes crucial for complex task execution. However, existing systems face challenges in multimodal information fusion, adaptive communication, and decision interpretability. To address these limitations, we propose a collaborative Conversational Embodied Intelligence Network (CC-EIN) integrating multimodal feature fusion, adaptive semantic communication, task coordination, and interpretability. PerceptiNet performs cross-modal fusion of image and radar data to generate unified semantic representations. An adaptive semantic communication strategy dynamically adjusts coding schemes and transmission power according to task urgency and channel quality. A semantic-driven collaboration mechanism further supports task decomposition and conflict-free coordination among heterogeneous devices. Finally, the InDec module enhances decision transparency through Grad-CAM visualization. Simulation results in post-earthquake rescue scenarios demonstrate that CC-EIN achieves 95.4% task completion rate and 95% transmission efficiency while maintaining strong semantic consistency and energy efficiency. |
| title | Agentic AI-Empowered Conversational Embodied Intelligence Networks in 6G |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.19865 |