  This paper presents a novel Stochastic Optimal Control (SOC) method based on
Model Predictive Path Integral control (MPPI), named Stein Variational Guided
MPPI (SVG-MPPI), designed to handle rapidly shifting multimodal optimal action
distributions. While MPPI can find a Gaussian-approximated optimal action
distribution in closed form, i.e., without iterative solution updates, it
struggles with the multimodality of the optimal distributions. This is due to
the less representative nature of the Gaussian. To overcome this limitation,
our method aims to identify a target mode of the optimal distribution and guide
the solution to converge to fit it. In the proposed method, the target mode is
roughly 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 the
fast convergence property of MPPI. Our simulation and real-world experimental
results demonstrate that SVG-MPPI outperforms both the original MPPI and other
state-of-the-art sampling-based SOC algorithms in terms of path-tracking and
obstacle-avoidance capabilities. Source code:
https://github.com/kohonda/proj-svg_mppi
