Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning

The paper introduces Isaac‑Net, a GPU‑batched 5G New Radio module that integrates a simulated 5G network into massively parallel robot learning environments. It simulates every 0.5 ms slot for thousands of environments simultaneously, matching the median delay and Age of Information of the ns‑3 5G‑LENA simulator while enabling up to one million robots on a single GPU. Extensive experiments show that Isaac‑Net maintains network‑in‑the‑loop performance at 83 % of the Isaac Lab rate without the network, validating its scalability and fidelity.

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• robotics