[Submitted on 2 Sep 2026]
Title:Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework
View a PDF of the paper titled Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework, by Zekai Jin and 3 other authors
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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.
Comments: 22 pages, 11 figures, 10 tables. Published in Advanced Engineering Informatics
Subjects:
Robotics (cs.RO); Machine Learning (stat.ML)
Cite as: arXiv:2609.13234 [cs.RO]
(or arXiv:2609.13234v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.13234
arXiv-issued DOI via DataCite
Journal reference: Advanced Engineering Informatics 76, Part B (2026) 104823
Related DOI:
https://doi.org/10.1016/j.aei.2026.104823
DOI(s) linking to related resources
Submission history
From: Zekai Jin [view email] [v1] Wed, 2 Sep 2026 12:51:47 UTC (4,005 KB)
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