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 multimodality of the optimal distributions, such as those
involving non-convex constraints for obstacle avoidance. 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_mppiComment: 7 pages, 5 figure