Recent Advances in Path Integral Control for Trajectory Optimization: An Overview in Theoretical and Algorithmic Perspectives

Authors: Muhammad Kazim, JunGee Hong, Min-Gyeom Kim, Kwang-Ki K. Kim

Published: 2023 (Survey Paper)

Source: Annual Reviews in Control

arXiv: 2309.12566

DOI: 10.1016/j.arcontrol.2023.100931

Summary

Survey paper reviewing the theoretical foundations and algorithmic advances in path integral control methods for trajectory optimization, covering MPPI variants, variational inference approaches, and connections to stochastic optimal control theory.

Abstract

This paper presents a tutorial overview of path integral (PI) approaches for stochastic optimal control and trajectory optimization. We concisely summarize the theoretical development of path integral control to compute a solution for stochastic optimal control and provide algorithmic descriptions of the cross-entropy (CE) method, an open-loop controller using the receding horizon scheme known as the model predictive path integral (MPPI), and a parameterized state feedback controller based on the path integral control theory. We discuss policy search methods based on path integral control, efficient and stable sampling strategies, extensions to multi-agent decision-making, and MPPI for the trajectory optimization on manifolds. For tutorial demonstrations, some PI-based controllers are implemented in Python, MATLAB and ROS2/Gazebo simulations for trajectory optimization. The simulation frameworks and source codes are publicly available at https://github.com/i-ASL/An-Overview-on-Recent-Advances-in-Path-Integral-Control

Links

Primary

Paper arxiv.org

Standard

arXiv Abstract 2309.12566 arXiv PDF 2309.12566 arXiv HTML 2309.12566 DOI 10.1016/j.arcontrol.2023.100931

Alternate

sciencedirect.com sciencedirect.com GitHub: i-ASL/An-Overview-on-Recent-Advances-in-Path-Integral-Control github.com

Tags

• MPPI

• Path integral control

• Survey

• Trajectory optimization

• Stochastic optimal control

• Variational inference