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This paper presents a novel Stochastic Optimal Control (SOC) method based onModel Predictive Path Integral control (MPPI), named Stein Variational GuidedMPPI (SVG-MPPI), designed to handle rapidly shifting multimodal optimal actiondistributions. While MPPI can find a Gaussian-approximated optimal actiondistribution in closed form, i.e., without iterative solution updates, itstruggles with multimodality of the optimal distributions, such as thoseinvolving non-convex constraints for obstacle avoidance. This is due to theless representative nature of the Gaussian. To overcome this limitation, ourmethod aims to identify a target mode of the optimal distribution and guide thesolution to converge to fit it. In the proposed method, the target mode isroughly estimated using a modified Stein Variational Gradient Descent (SVGD)method and embedded into the MPPI algorithm to find a closed-form"mode-seeking" solution that covers only the target mode, thus preserving thefast convergence property of MPPI. Our simulation and real-world experimentalresults demonstrate that SVG-MPPI outperforms both the original MPPI and otherstate-of-the-art sampling-based SOC algorithms in terms of path-tracking andobstacle-avoidance capabilities. Source code:https://github.com/kohonda/proj-svg_mppi

Stein Variational Guided Model Predictive Path Integral Control: Proposal and Experiments with Fast Maneuvering Vehicles