发表机构
Centre for Wireless Communications, University of Oulu（奥卢大学无线通信中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出耦合控制与无线JEPA世界模型的远程机器人控制框架，结合自适应弹性机制，在仿真环境中较传统方法显著提升通信效率、鲁棒性与弹性，同时维持导航性能。
AI 中文摘要
在无线网络上运行的远程机器人系统，必须在通信资源有限、信道条件变化和环境变化的情况下保持可靠控制。持续传输高维感官观测（如相机图像）会增加通信开销和能源消耗，同时降低不可靠链路下的鲁棒性。为应对这些挑战，本文提出一种弹性、感知通信的远程机器人控制框架，该框架基于耦合控制与无线联合嵌入预测架构（JEPA）世界模型，从视觉观测以及基于频谱图和持久性图像（PIs）的原始与结构化射频（RF）表示的组合中，联合捕获机器人动力学和无线信道演化。学习到的潜在表示可通过联合预测未来机器人状态和无线条件实现预测性通信调度，从而减少不必要的上行链路传输，同时保持可靠控制。此外，一种自适应弹性机制可检测潜在预测差异，并高效调整感知嵌入以适应无线和视觉环境变化，无需重新训练完整控制策略。所提框架在同步Gazebo-机器人操作系统（ROS）-Sionna机器人-无线仿真环境中，在不同无线传播和感知条件下进行评估。结果表明，与传统比例积分微分（PID）、无模型深度Q网络（DQN）以及基于视觉Transformer（ViTs）的预测方法相比，该框架在保持导航性能的同时，通信效率、鲁棒性和弹性均有显著提升。
英文摘要
Remote robotic systems operating over wireless networks must maintain reliable control despite limited communication resources, changing channel conditions, and environmental disturbances.However, continuously transmitting high-dimensional sensory observations, such as camera images, increases communication overhead and energy consumption while reducing robustness under unreliable connectivity.To address these challenges, this paper proposes a resilient communication-aware remote robotic control framework based on coupled control and wireless Joint Embedding Predictive Architecture (JEPA) world models that jointly capture robot dynamics and wireless channel evolution from visual observations and a combination of raw and structured radio frequency (RF) representations based on spectrograms and Persistence Images(PIs).The learned latent representations enable predictive communication scheduling by jointly forecasting future robot states and wireless conditions, thereby reducing unnecessary uplink transmissions while maintaining reliable control performance.Furthermore, an adaptive resilience mechanism detects latent prediction discrepancies and efficiently adapts perception embeddings to accommodate wireless and visual environmental changes without retraining the complete control policy.The proposed framework is evaluated in a synchronized Gazebo-Robot Operating System (ROS)-Sionna robot-wireless simulation environment under diverse wireless propagation and perception perturbations.Experimental results demonstrate significant improvements in communication efficiency, robustness, and resilience while maintaining navigation performance compared with conventional Proportional Integral Derivative (PID), model-free Deep Q-Network (DQN), and predictive approaches based on Vision Transformers(ViTs).
Comments13 pages, 13 figures. Submitted to IEEE Internet of Things Journal
