Haolong Jiang | Searcharxiv

arXiv2026

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

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.

eess.SY↗

arXiv2025

DBaS-Log-MPPI: Efficient and Safe Trajectory Optimization via Barrier States

Optimizing trajectory costs for nonlinear control systems remains a significant challenge. Model Predictive Control (MPC), particularly sampling-based approaches such as the Model Predictive Path Integral (MPPI) method, has recently demonstrated considerable success by leveraging parallel computing to efficiently evaluate numerous trajectories. However, MPPI often struggles to balance safe navigation in constrained environments with effective exploration in open spaces, leading to infeasibility in cluttered conditions. To address these limitations, we propose DBaS-Log-MPPI, a novel algorithm that integrates Discrete Barrier States (DBaS) to ensure safety while enabling adaptive exploration with enhanced feasibility. Our method is efficiently validated through three simulation missions and one real-world experiment, involving a 2D quadrotor and a ground vehicle navigating through cluttered obstacles. We demonstrate that our algorithm surpasses both Vanilla MPPI and Log-MPPI, achieving higher success rates, lower tracking errors, and a conservative average speed.

cs.RO↗