优化腿部机器人运动的采样策略，提升MPPI控制效果。
Sampling Strategy Design for Model Predictive Path Integral Control on Legged Robot Locomotion
- 采用结构化参数化设计多种采样策略，对比其性能差异。
- 仿真验证显示，合理采样策略显著提升运动平滑性与任务成功率。
- 适合研究机器人运动规划与强化学习控制的学者参考。
模型预测路径积分（MPPI）是一种强大的基于采样的最优控制方法，适用于复杂、非线性且高维的系统。然而，直接将MPPI应用于腿部机器人系统面临诸多挑战。本文系统研究了在MPPI框架内采样策略设计对腿部机器人步态控制的影响。基于结构化控制参数化思想，探索并比较了多种采样策略，包括无结构和基于样条的方法。通过在四足机器人平台上的大量仿真，评估了不同采样策略对控制平滑性、任务表现、鲁棒性和采样效率的影响。结果为在复杂腿部系统中部署MPPI提供了新的实践洞见。
原文摘要 · Abstract (English)
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.
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