Control

Model Predictive Path Integral (MPPI)

Model predictive path integral (MPPI) control is a sampling-based model predictive control algorithm, developed by Williams et al., that rolls out many stochastically perturbed control sequences through a dynamics model and updates the nominal plan with an exponentially reward-weighted average. Derivative-free and trivially parallelizable on GPUs, it handles non-differentiable dynamics and costs, and was demonstrated in aggressive off-road autonomous driving on the AutoRally platform.

Why it matters for physical AI

Sampling-based planning composes naturally with learned neural dynamics models and world models, since it needs only forward rollouts, making it a workhorse for model-based control with black-box simulators.