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arXiv Machine Learning By H. P. Madushanka, Sumudu Samarakoon, Mehdi Bennis

Coupled Control and Wireless World Models for Resilient Remote Robotic Control

Read the original on arXiv Machine Learning →

The paper introduces a resilient remote robotic control framework that couples control and wireless world models using a Joint Embedding Predictive Architecture (JEPA). By learning latent representations from visual observations and radio frequency (RF) data, the system predicts future robot states and wireless conditions to schedule uplink transmissions efficiently. An adaptive resilience mechanism adjusts perception embeddings when prediction errors arise, enabling robust operation without retraining the entire control policy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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