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Life sciences · Preprint
arXiv · September 4, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint describes a proposed framework combining coupled control and wireless world models (JEPA) to improve communication efficiency and robustness in remote robotic systems. Evaluation is limited to simulation under diverse wireless and perception perturbations, with claimed improvements over PID, DQN, and Vision Transformer baselines; however, the work has not undergone peer review and lacks real-world validation or quantified performance metrics.
Simulation-based proof-of-concept study. Simulated remote robotic system operating over wireless networks with limited communication resources and changing channel conditions.. Intervention: Coupled control and wireless Joint Embedding Predictive Architecture (JEPA) world models that jointly capture robot dynamics and wireless channel evolution from visual observations and radio frequency (RF) representations; adaptive resilie…. Compared with: Conventional Proportional Integral Derivative (PID), model-free Deep Q-Network (DQN), and predictive approaches based on Vision Transformers (ViTs)..
Framework demonstrates improvements in communication efficiency, robustness, and resilience compared to PID, DQN, and ViT-based approaches in simulation. Adaptive resilience mechanism detects latent prediction discrepancies and adapts perception embeddings without retraining the complete control policy. Navigation performance maintained while reducing communication overhead under diverse wireless propagation and perception perturbations.
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This is an unrefereed preprint presenting a novel engineering framework for remote robotic control; it reports simulation results but lacks peer review and clinical or real-world validation.
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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).
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