用分层嵌入预测架构，让远程控制在低带宽下也能稳定运行。
Hierarchical JEPA Meets Predictive Remote Control in Beyond 5G Networks
- 分三级预测：高层长期、中层插值、底层精细，提升预测稳定性
- 实测支持设备数提升42.83%，不降低控制性能
- 适合带宽受限的工业远程控制场景
在无线网络化控制系统中，确保分布式设备向远程控制器及时可靠地传输状态至关重要。然而，当多个设备通过带宽受限的无线网络传输高维状态（如图像或视频帧）时，通信效率与控制性能之间出现关键权衡。为此，我们提出一种分层联合嵌入预测架构（H-JEPA），用于可扩展的预测控制。设备观测被编码为低维嵌入以保留关键动态，而非直接传输状态。该架构采用三级分层预测机制，分别在不同时间分辨率下运行：高层预测实现长期稳定性，中层完成中间插值，底层进行细粒度修正。控制动作在嵌入空间内生成，无需状态重构。在倒立摆系统上的仿真结果表明，H-JEPA 在有限无线容量下可支持最多增加42.83%的设备，且不牺牲控制性能。
原文摘要 · Abstract (English)
In wireless networked control systems, ensuring timely and reliable state updates from distributed devices to remote controllers is essential for robust control performance. However, when multiple devices transmit high-dimensional states (e.g., images or video frames) over bandwidth-limited wireless networks, a critical trade-off emerges between communication efficiency and control performance. To address this challenge, we propose a Hierarchical Joint-Embedding Predictive Architecture (H-JEPA) for scalable predictive control. Instead of transmitting states, device observations are encoded into low-dimensional embeddings that preserve essential dynamics. The proposed architecture employs a three-level hierarchical prediction, with high-level, medium-level, and low-level predictors operating across different temporal resolutions, to achieve long-term prediction stability, intermediate interpolation, and fine-grained refinement, respectively. Control actions are derived within the embedding space, removing the need for state reconstruction. Simulation results on inverted cart-pole systems demonstrate that H-JEPA enables up to 42.83 % more devices to be supported under limited wireless capacity without compromising control performance.
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