用优化路径积分提升足式机器人实时运动控制效率
Control of Legged Robots using Model Predictive Optimized Path Integral
- 融合MPPI与交叉熵、协方差自适应的采样优化策略
- 相同任务下仅需更少样本即达成更优行走表现
- 适合需要快速迭代的复杂地形足式机器人控制
足式机器人具备穿越崎岖地形和复杂环境的独特能力，适用于现实世界中的非结构化场景。然而其性能仍不及自然系统。近期基于采样的预测控制器展现出显著潜力。本文提出一种结合模型预测路径积分（MPPI）与交叉熵（CE）、协方差矩阵自适应（CMA）方法的采样式模型预测策略，用于生成四足机器人在多场景下的实时全身运动。实验表明，该方法（称作MPOPI）兼具三者优势，显著提升采样效率，在相同条件下以更少样本实现更优的运动表现。在多个场景的仿真测试中，MPOPI可作为任意时间控制策略，每次迭代均能增强机器人的运动能力。
原文摘要 · Abstract (English)
Legged robots possess a unique ability to traverse rough terrains and navigate cluttered environments, making them well-suited for complex, real-world unstructured scenarios. However, such robots have not yet achieved the same level as seen in natural systems. Recently, sampling-based predictive controllers have demonstrated particularly promising results. This paper investigates a sampling-based model predictive strategy combining model predictive path integral (MPPI) with cross-entropy (CE) and covariance matrix adaptation (CMA) methods to generate real-time whole-body motions for legged robots across multiple scenarios. The results show that combining the benefits of MPPI, CE and CMA, namely using model predictive optimized path integral (MPOPI), demonstrates greater sample efficiency, enabling robots to attain superior locomotion results using fewer samples when compared to typical MPPI algorithms. Extensive simulation experiments in multiple scenarios on a quadruped robot show that MPOPI can be used as an anytime control strategy, increasing locomotion capabilities at each iteration.
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