将边缘智能与近场通信结合，提升机器人实时计算与通信效率。
Embodied Edge Intelligence Meets Near Field Communication: Concept, Design, and Verification
- 通过近场通信增强边缘智能的计算与传输能力
- 实测验证新方法在低延迟和高安全下性能更优
- 适合需实时响应的机器人协同系统研究者
实现具身人工智能面临大型模型（LMs）巨大算力需求的挑战。为支持大型模型并确保实时推理，具身边缘智能（EEI）是一种有前景的范式，它利用靠近具身机器人的边缘计算节点提供算力。由于具身数据交互需求，EEI需要更高的频谱效率、更强的通信安全性和更低的用户间干扰。近场通信（NFC）凭借其超大规模天线阵列硬件基础，成为理想解决方案。因此，本文倡导将EEI与NFC融合，提出近场具身边缘智能（NEEI）范式。然而，NEEI也引入了孤立的EEI或NFC设计无法解决的新挑战，为两者功能联合优化创造了研究机会。为此，我们提出面向EEI辅助的NFC场景的射频友好具身规划，以及面向NFC辅助的EEI场景的视觉引导波束聚焦。同时阐明如何通过机会性协同导航实现资源高效的NEEI。实验结果验证了所提技术相比多种基准的优越性。
原文摘要 · Abstract (English)
Realizing embodied artificial intelligence is challenging due to the huge computation demands of large models (LMs). To support LMs while ensuring real-time inference, embodied edge intelligence (EEI) is a promising paradigm, which leverages an LM edge to provide computing powers in close proximity to embodied robots. Due to embodied data exchange, EEI requires higher spectral efficiency, enhanced communication security, and reduced inter-user interference. To meet these requirements, near-field communication (NFC), which leverages extremely large antenna arrays as its hardware foundation, is an ideal solution. Therefore, this paper advocates the integration of EEI and NFC, resulting in a near-field EEI (NEEI) paradigm. However, NEEI also introduces new challenges that cannot be adequately addressed by isolated EEI or NFC designs, creating research opportunities for joint optimization of both functionalities. To this end, we propose radio-friendly embodied planning for EEI-assisted NFC scenarios and view-guided beam-focusing for NFC-assisted EEI scenarios. We also elaborate how to realize resource-efficient NEEI through opportunistic collaborative navigation. Experimental results are provided to confirm the superiority of the proposed techniques compared with various benchmarks.
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