{
  "abstract": "Octopus crawling motivates soft robots that exploit redundancy, yet discovering and organizing diverse coordination modes for adaptation remains challenging. To address this, we introduce a Diffusion-based Uncertainty-aware Optimization (DUO) algorithm that learns demonstration-free crawling controllers for a simulated, muscle-actuated CyberOctopus. This work represents the first application of diffusion-based control to soft multi-arm robots in contact-rich simulations. By embedding a variety of locomotion behaviors within a shared control distribution, this approach enables the simulated octopus to navigate dynamic physical constraints, demonstrating that learned coordination diversity inherently facilitates robust adaptation. The main contributions include: (i) a symmetry-structured policy representation that folds radially equivalent controllers into a canonical directional sector, (ii) an online black-box optimization strategy, the DUO algorithm, that discovers and retains diverse coordination modes, and (iii) a control editing technique that adapts existing controllers to novel actuator constraints without retraining. These results show how learned coordination diversity makes motor abundance a practical resource for adaptation in soft multi-arm robots.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.21138",
  "author_total": 5,
  "authors": [
    {
      "name": "Seung Hyun Kim"
    },
    {
      "name": "Heng-Sheng Chang"
    },
    {
      "name": "Kimia Kazemi"
    },
    {
      "name": "Prashant Mehta"
    },
    {
      "name": "Mattia Gazzola"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9aed9nbsm7tpm.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.21138",
  "fulltext_url": null,
  "id": "9aed9nbsm7tpm",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.21138",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.RO"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2204|Soft Robotics and Applications"
    ]
  ],
  "openalex": "W7213892180",
  "pdf_url": "https://arxiv.org/pdf/2609.21138",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9aed9nbsm7tpm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:07Z",
      "native_id": "2609.21138",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.21138"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Diverse and Adaptable Arm Coordination for Octopus-Crawling via Diffusion-Based Uncertainty-Aware Optimization",
  "title_text": "Diverse and Adaptable Arm Coordination for Octopus-Crawling via Diffusion-Based Uncertainty-Aware Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:07Z",
  "url": "https://research.draconitic.com/paper/9aed9nbsm7tpm",
  "venue": "arXiv",
  "versions": []
}