Motivation: sampling-based MPC lacks of + theoretical understanding
++ Sampling-based MPC becomes prevalent in motion planning and model-based RL for its + flexibility and parallizability. +
++ The following figure shows different sampling strategy of CoVO-MPC and MPPI controlling a 2d + drone. Both algorithm do a receding-horizon control by sampling trajectories (the green + area) at each time step. +
++ However, there is no convergence analysis to it, which leads to tune hyperparameters + heuristically. For instance, MPPI use dynamic-agnostic isotropic Gaussian to sample + trajectories, which leads to sub-optimal performance. +
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