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Comparative Analysis of Four Planners in the Same Loop for MuJoCo MPC

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GuYueJu published an analysis of the MuJoCo real-time predictive control framework, stating that the framework uses residual costs to unify task descriptions, and that four planners—predictive sampling, gradient descent, iLQG, and hybrid ILQS—replan at every step within the same control loop. In a Cartpole comparative experiment, the analysis presents comparison results for the convergence speed and per-step computation of the four planners.

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  1. Analysis of Sampling and Derivative Predictive Control for Real-Time Behavior Synthesis in MuJoCo MPC

    Gu Yue Ju analyzes the MuJoCo real-time predictive control framework, which uses a residual cost to unify task descriptions, with four planners—predictive sampling, gradient descent, iLQG, and hybrid ILQS—replanning at every step within the same loop. A Cartpole comparison experiment reports the convergence speed and per-step computational cost of the four.

    Guyuehome ·

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