{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14965",
  "author_total": 3,
  "authors": [
    {
      "name": "Morteza Kimiaei"
    },
    {
      "name": "Roya Khalili-Amirabadi"
    },
    {
      "name": "Mohsen Jalaeian-Farimani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7a0041652a705.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14965",
  "fulltext_url": null,
  "id": "7a0041652a705",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14965",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2207|Advanced Control Systems Optimization"
    ]
  ],
  "openalex": "W7221780930",
  "pdf_url": "https://arxiv.org/pdf/2610.14965",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7a0041652a705.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:15Z",
      "native_id": "2610.14965",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14965"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Risk-Constrained MPPI Tuning with Update-Aware MA-ES Racing",
  "title_text": "Risk-Constrained MPPI Tuning with Update-Aware MA-ES Racing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:15Z",
  "url": "https://research.draconitic.com/paper/7a0041652a705",
  "venue": "arXiv",
  "versions": []
}