用高斯-牛顿法加速采样控制，提升高维问题求解效率。
Gauss-Newton accelerated MPPI Control
- 引入雅可比重构与二阶高斯-牛顿法改进采样优化
- 在高维场景下显著提升计算效率与可扩展性
- 适合需快速稳定控制的机器人与强化学习应用
模型预测路径积分（MPPI）是一种基于采样的优化方法，近年来在机器人学和强化学习领域受到关注。MPPI被广泛用于确定性直接单次射击最优控制问题的GPU加速随机搜索，具有灵活性、鲁棒性、易实现性和天然并行性等优点。然而，在高维设置下，其性能会因蒙特卡洛采样而下降。本文提出一种增强型MPPI方法，结合雅可比重构技术和二阶广义高斯-牛顿法，称为‘高斯-牛顿加速MPPI’。数值结果表明，该方法显著提升了MPPI的可扩展性与计算效率，同时保留了经典MPPI的核心优势，使其在高维问题中依然表现优异。
原文摘要 · Abstract (English)
Model Predictive Path Integral (MPPI) control is a sampling-based optimization method that has recently attracted attention, particularly in the robotics and reinforcement learning communities. MPPI has been widely applied as a GPU-accelerated random search method to deterministic direct single-shooting optimal control problems arising in model predictive control (MPC) formulations. MPPI offers several key advantages, including flexibility, robustness, ease of implementation, and inherent parallelizability. However, its performance can deteriorate in high-dimensional settings since the optimal control problem is solved via Monte Carlo sampling. To address this limitation, this paper proposes an enhanced MPPI method that incorporates a Jacobian reconstruction technique and the second-order Generalized Gauss-Newton method. This novel approach is called \textit{Gauss-Newton accelerated MPPI}. The numerical results show that the Gauss-Newton accelerated MPPI approach substantially improves MPPI scalability and computational efficiency while preserving the key benefits of the classical MPPI framework, making it a promising approach even for high-dimensional problems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。