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

2026-01-04

arXiv.org

AI Summary

This work addresses the challenges of directly applying Model Predictive Path Integral (MPPI) control to legged robots, which often suffer from poor control smoothness, suboptimal task performance, weak robustness, and low sample efficiency. The study systematically investigates sampling strategy design within the MPPI framework and proposes a structured control parameterization approach, comparing unstructured random sampling against spline-based sampling. Extensive simulations demonstrate that spline-based sampling significantly enhances control smoothness, task success rates, and sample efficiency. These findings offer critical design principles and novel insights for the effective deployment of MPPI in complex legged robotic systems.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Model Predictive Path Integral
• legged robot locomotion
• sampling strategy
• optimal control
• nonlinear systems

Innovation

• sampling strategy
• Model Predictive Path Integral control
• structured control parameterization
• legged robot locomotion
• spline-based sampling