{
  "abstract": "This paper presents a distribution-agnostic robust trajectory-optimization framework based on chance-constrained reinforcement learning. The uncertainty is represented here through initial conditions and process noise, with the only requirement being that it can be sampled. A deterministic nominal trajectory is first computed offline, and reinforcement learning is then used only to robustify that baseline through a structured affine closed-loop correction law comprising a feedforward control adjustment and time-varying feedback gains. Probabilistic feasibility is enforced empirically through rollout-based upper-tail quantiles, while terminal dispersion is regulated through covariance-feasibility penalties. The framework is assessed on two materially different trajectory design problems. The flagship case study is a three-dimensional multi-impulse Earth-Mars transfer, where the learned policy is benchmarked against a recent robust trajectory-optimization reference under Gaussian uncertainty and then evaluated under bounded uniform uncertainty and under process disturbances not seen during training. The second case study is a stochastic atmospheric pinpoint rocket landing problem, used to assess portability to a short-horizon continuous-thrust setting with drag, mass depletion, and glide-slope constraints. The results show that the proposed framework can remain competitive in upper-tail fuel cost while preserving probabilistic feasibility, and that the same robustification scaffold can be carried across heterogeneous spacecraft trajectory planning problems without redesign of its core stochastic-control structure.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Aerospace Science and Technology 179 (2026) 113521",
  "arxiv": "2606.13605",
  "author_total": 4,
  "authors": [
    {
      "name": "Yashdeep Chaudhary"
    },
    {
      "name": "Roberto Armellin"
    },
    {
      "name": "Harry Holt"
    },
    {
      "name": "Marco Sagliano"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3ht4fxpdynces.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.13605",
  "fulltext_url": null,
  "id": "3ht4fxpdynces",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.13605",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.13605",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3ht4fxpdynces.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:48Z",
      "native_id": "2606.13605",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.13605"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning",
  "title_text": "Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:48Z",
  "url": "https://research.draconitic.com/paper/3ht4fxpdynces",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.ast.2026.113521",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}