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[Submitted on 15 Oct 2026] Title:Task Structure and Scalability in Multi-Robot Authoring View a PDF of the paper titled Task Structure and Scalability in Multi-Robot Authoring, by Christian Marowski and 1 other authors View PDF HTML (experimental) Abstract:Multi-robot authoring can reduce completion time by distributing drawing work, but the resulting speedup depends on task structure and the allocation-routing pipeline. We investigate these relationships by representing drawings as geometric stroke graphs and comparing component-level (single-token) allocation with shared-token (decomposed) allocation, which permits multiple robots to work within a connected component. A simulation study of 385 drawings and teams of one to eight robots shows diminishing speedup gains under single-token allocation and larger mean speedups under the shared-token pipeline, each normalized by its own single-robot completion time. Shared-token execution also exhibits lower idle-time shares at larger team sizes, although it does not reduce completion time for every drawing. Comparisons across structural attribute groups reveal associations between structural descriptors and differences in scaling behavior, with greater separation between several attribute groups at larger team sizes. These findings characterize empirical scaling in the evaluated pipelines and highlight the importance of considering within-component workload and graph structure alongside component count. Additional materials are available at this https URL. Subjects: Robotics (cs.RO) Cite as: arXiv:2610.22670 [cs.RO] (or arXiv:2610.22670v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.22670 arXiv-issued DOI via DataCite (pending registration) Submission history From: Christian Marowski [view email] [v1] Thu, 15 Oct 2026 21:37:50 UTC (1,634 KB) Full-text links: Access Paper: View a PDF of the paper titled Task Structure and Scalability in Multi-Robot Authoring, by Christian Marowski and 1 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO new | recent | 2026-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)