Sampling Strategy Design for Model Predictive Path Integral Control on Legged Robot Locomotion

TL;DR

This paper investigates various sampling strategies within the MPPI control framework to improve legged robot locomotion, focusing on control smoothness, robustness, and efficiency through extensive simulation studies.

Contribution

It systematically compares structured and unstructured sampling strategies for MPPI in legged robots, providing practical insights for control performance enhancement.

Findings

Spline-based sampling improves control smoothness.

Structured sampling enhances robustness.

Sampling strategy significantly impacts sample efficiency.

Abstract

Model Predictive Path Integral (MPPI) control has emerged as a powerful sampling-based optimal control method for complex, nonlinear, and high-dimensional systems. However, directly applying MPPI to legged robotic systems presents several challenges. This paper systematically investigates the role of sampling strategy design within the MPPI framework for legged robot locomotion. Based upon the idea of structured control parameterization, we explore and compare multiple sampling strategies within the framework, including both unstructured and spline-based approaches. Through extensive simulations on a quadruped robot platform, we evaluate how different sampling strategies affect control smoothness, task performance, robustness, and sample efficiency. The results provide new insights into the practical implications of sampling design for deploying MPPI on complex legged systems.