外观
论文arXiv 2610.1496518 天前
基于更新感知 MA-ES Racing 的风险约束 MPPI 调参
原文链接：Risk-Constrained MPPI Tuning with Update-Aware MA-ES Racing（arXiv 2610.14965 · 发布日期：2026-10-10）
Risk-Constrained MPPI Tuning with Update-Aware MA-ES Racing
将MPPI离线调参表述为机会约束黑盒优化，基于更新感知MA-ES并在三类车辆模型数值实验中验证。
核心要点
- 针对有噪声控制器评估下的MPPI离线调参，论文将其建模为机会约束黑盒优化问题。
- 方法利用评估统计信息判断进化更新可靠性，并在重组方向获认证后用无导数NMLS细化候选解。
- 论文建立在线MPPI控制和离线调参的最坏情形算术复杂度界，并设置独立验证阶段评估冻结控制器。
- 数值实验显示NMLS细化与标准MA-ES有竞争力，可跨三类车辆模型迁移，并优于所测VRBBO配置。
验证范围与局限：验证范围为TurtleBot 4、Jackal-like和Ackermann模型上的数值实验，未声称普遍优越或硬件级安全。
英文摘要 (Abstract)
We formulate offline tuning of Model Predictive Path Integral (MPPI) controllers as a chance-constrained black-box optimization problem and develop an update-aware framework based on Matrix Adaptation Evolution Strategy (MA-ES). We use statistical information from noisy controller evaluations to determine when the evolutionary update is sufficiently reliable. When the recombination direction is certified, a derivative-free nonmonotone line search (NMLS) is used to refine the candidate solution, and a separate validation stage assesses the final frozen controller. We establish a worst-case arithmetic-complexity bound for both online MPPI control and offline tuning. Numerical experiments on TurtleBot 4, Jackal-like, and Ackermann models show that the proposed NMLS refinement is competitive with standard MA-ES, transfers across the three vehicle models without model-specific retuning, and improves several cost and validation measures. Both MA-ES variants also compare favorably with the tested randomized derivative-free line search (VRBBO) configuration. The results support the practical applicability and transferability of the proposed framework, while not claiming universal superiority or hardware-level safety.
作者：Morteza Kimiaei, Roya Khalili-Amirabadi, Mohsen Jalaeian-F.
来源
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