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arXiv · 2610.14965

Risk-Constrained MPPI Tuning with Update-Aware MA-ES Racing

Morteza Kimiaei·Roya Khalili-Amirabadi·Mohsen Jalaeian-Farimani
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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.

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Morteza Kimiaei, Roya Khalili-Amirabadi, Mohsen Jalaeian-Farimani. 2026-10-10. Risk-Constrained MPPI Tuning with Update-Aware MA-ES Racing. https://arxiv.org/abs/2610.14965

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