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arXiv:2605.15480 · detector doi_compliance · incontrovertible · 2026-05-19 14:37:41.651882+00:00

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Zhang, H., Yang, Y., and Jiang, Y. (2021). Reinforcement learning-based control with communication delays: The- ory and applications.IEEE Transactions on Cybernet- ics, 51(9), 4368–4381. doi:10.1109/TCYB.2020.2988820. Appendix A. TRAINING PROCEDURE The proposed framework is trained in two stages, both conducted entirely within a MuJoCo simulation of the Franka Panda manipulator. No real-robot data is used during training; the physical experiments described in Section 4.2 therefore also serve as a sim-to-real evaluation. Training and validation environments are instantiated from distinct random seeds to prevent data leakage. T raining data and collection.The training data con- sists of figure-8 reference trajectories generated on-the-fly by the leader simulator, with their geometric and temporal parameters randomized at each episode reset (centerc x ∈ [0.3,0.4] m,c y ∈[−0.1,0.1] m, scales x,y ∈[0.1,0.3] m, sz ∈[0.01,0.03] m, frequencyf∈[0.05,0.15] Hz). At every control step, the leader joint state, the corresponding stochastic delayω t s, and the future ground-truth trajectory used as the autoregressive target are written into a circular replay buffer. This online collection scheme is chosen over a pre-collected fixed dataset for two reasons. First, the joint distribution of trajectory shape and delay realization is too high-dimensional to enumerate offline; sampling on-the-fly ensures uniform coverage of the operating envelope used at deployment. Second, the autoregressive ta

Evidence payload

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