{
  "abstract": "Autonomous capture of a tumbling object with a space manipulator requires trajectory generation under real-time onboard constraints. However, coupled bus-arm dynamics, rotating safety geometry, and visibility requirements make the underlying optimization severely nonconvex, hence expensive to solve and sensitive to initialization. This paper develops a causal-transformer warm-start for the computationally dominant attitude-manipulator stage of a two-stage sequential convex programming (SCP) framework. Across 300 held-out scenarios evaluated on embedded hardware, the learned warm-start reduces the iterations of the second-stage SCP by up to 32% and runtime by 16% while maintaining numerical feasibility and matching the final control cost. Furthermore, linear and flow matching action decoders are compared across action-chunk lengths and training dataset sizes, where the flow matching decoder exhibits stronger training-data efficiency than the linear decoder. When SCP is terminated based on feasibility, the runtime is further reduced while the learned prior suppresses the severe high-cost tail observed with heuristic initialization. These results demonstrate that learned warm-starts can reduce onboard optimization cost while steering SCP toward basins containing feasible, low-cost solutions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.17317",
  "author_total": 4,
  "authors": [
    {
      "name": "Yuji Takubo"
    },
    {
      "name": "Maximilian Adang"
    },
    {
      "name": "Mac Schwager"
    },
    {
      "name": "Simone D'Amico"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ck61w96kja217.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.17317",
  "fulltext_url": null,
  "id": "ck61w96kja217",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.17317",
  "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.AI"
    ],
    [
      "arxiv",
      "cs.RO"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2202|Space Satellite Systems and Control"
    ]
  ],
  "openalex": "W7165051045",
  "pdf_url": "https://arxiv.org/pdf/2606.17317",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ck61w96kja217.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:27Z",
      "native_id": "2606.17317",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.17317"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators",
  "title_text": "Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:27Z",
  "url": "https://research.draconitic.com/paper/ck61w96kja217",
  "venue": "arXiv",
  "versions": []
}