A simplified representation of the MPPI algorithm during each optimization iteration. For clarity, we only visualize one sampled trajectory (in green). (a) Amount of changes between previously computed control sequence and the next control sequence (along the “i-axis”). (b) Amount of changes in control values during MPPI rollouts (along the “t-axis”), which are hard to be minimized by the MPPI baseline. Such chattering in control input becomes more prominent in cases where the environment changes rapidly, possibly even causing the MPPI to
diverge.
To address this issue, we propose the Smooth MPPI algorithm that seamlessly combines MPPI with an input-lifting strategy.
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