面向动作感知无线边缘网络的边缘原生具身智能
Edge-Native Embodied Intelligence for Action-Aware Wireless Edge Networks
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中文总结 AI 辅助
该研究提出边缘原生具身智能（ENEI）框架，整合6G技术与具身智能，通过双向循环解决具身智能部署的资源与延迟问题，经案例验证可支撑自适应具身无线系统。
中文摘要 AI 辅助
具身智能正将人工智能从被动数字感知转向主动物理交互。然而，基于基础模型的具身智能体面临开放世界认知与资源受限部署之间的根本矛盾：设备端模型受限于计算、内存和能耗预算，而以云为中心的解决方案则会在动态无线链路上引入延迟和可靠性风险。边缘通用智能提供了有前景的认知主干，但现有框架仍缺乏物理接地、动作感知以及主动获取有用物理经验的机制。为解决这些局限，本文提出边缘原生具身智能（ENEI），这是一种动作感知无线边缘框架，将具身智能体、6G通信与网络架构、边缘认知服务整合为6G介导的双向边缘-具身循环。沿边缘到具身轴，置信度感知辅助与边缘驱动的生成式适配增强了分布外（OOD）条件下的本地自主性；沿具身到边缘轴，经验价值引导的主动具身联邦学习使物理动作为边缘模型的持续演进生成有用经验。6G架构通过面向目标的传输与可编程无线电资源分配支持这两个方向。针对OOD无人机导航与移动驱动联邦学习的两个案例研究，证明了所提机制的可行性与通信效率。ENEI提供了统一视角：边缘认知强化具身动作，而具身智能体主动丰富边缘认知，为可扩展、自适应、自演进的具身无线系统奠定基础。
英文摘要
Embodied intelligence is shifting artificial intelligence from passive digital perception toward active physical interaction. However, foundation-model-enabled embodied agents face a fundamental tension between open-world cognition and resource-constrained deployment. On-device models are limited by computation, memory, and energy budgets, whereas cloud-centric solutions introduce latency and reliability risks over dynamic wireless links. Edge general intelligence provides a promising cognitive backbone, but existing frameworks still lack physical grounding, action awareness, and mechanisms for actively acquiring useful physical experience. To address these limitations, this article introduces edge-native embodied intelligence (ENEI), an action-aware wireless edge framework that integrates embodied agents, the 6G communication and networking fabric, and edge cognitive services into a 6G-mediated bidirectional edge-embodiment loop. Along the edge-to-embodiment axis, confidence-aware assistance and edge-driven generative adaptation enhance local autonomy under out-of-distribution (OOD) conditions. Along the embodiment-to-edge axis, value-of-experience guided active embodied federated learning enables physical actions to generate informative experience for continuous edge model evolution. The 6G fabric supports both directions through goal-oriented transmission and programmable radio-resource allocation. Two case studies on OOD drone navigation and mobility-driven federated learning illustrate the feasibility and communication efficiency of the proposed mechanisms. ENEI provides a unified perspective in which edge cognition strengthens embodied action, while embodied agency actively enriches edge cognition, laying the foundation for scalable, adaptive, and self-evolving embodied wireless systems.
