{
  "abstract": "Industrialized construction imposes stringent precision requirements on robotic assembly of modular components such as prefabricated window units. In tolerance-critical operations, the central bottleneck is not only mechanical clearance but also converting tacit installer expertise into data-efficient autonomy under sparse acceptance feedback, contact variability, and millimeter-scale constraints. We present an installer-in-the-loop interactive reinforcement learning framework that acquires expertise through offline teleoperated demonstrations, sparse event-driven binary takeovers at contact-failure boundaries, and acceptance-aligned terminal rewards, logged under a unified schema for traceable offline-to-online adaptation. A temporally abstract action-sequence policy built on Q-chunking with Flow Q-Learning captures multimodal recovery maneuvers under sparse terminal rewards, while a non-updating warm-start phase stabilizes the offline-to-online transition. The framework is evaluated in MuJoCo across the workflow from suction acquisition through clearance-limited seating, under structured staging and end-to-end randomized placement. Within a defined stress-test regime with 2 mm per-side clearance, bounded pose perturbations, and friction randomization, the pipeline attains 100\\% autonomous seating with 12--15 min of cumulative installer supervision over 3.0 h of online training, and reaches the 95\\% success milestone in approximately 0.5 h and 1.5 h in the two experiments. We also report wall-clock adaptation time, cumulative takeover minutes, intervention-rate decay, and stage-wise failure attribution to inform supervision budgeting. Ablations isolate the complementary contributions of temporal abstraction, installer intervention, and warm-start value calibration.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Advanced Engineering Informatics 76, Part B (2026) 104823",
  "arxiv": "2609.13234",
  "author_total": 4,
  "authors": [
    {
      "name": "Zekai Jin"
    },
    {
      "name": "Huiguang Wang"
    },
    {
      "name": "Xiaoning Sun"
    },
    {
      "name": "Yi Shao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cew88cbgwytcc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13234",
  "fulltext_url": null,
  "id": "cew88cbgwytcc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13234",
  "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.RO"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.13234",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cew88cbgwytcc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:36Z",
      "native_id": "2609.13234",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13234"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework",
  "title_text": "Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:36Z",
  "url": "https://research.draconitic.com/paper/cew88cbgwytcc",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.aei.2026.104823",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}