{
  "abstract": "Joint failures can disrupt gait coordination, velocity tracking, and body stability in quadruped robots by reducing the available control authority. This study investigates faults in which joints remain locked at fixed angles and presents Adjudicate Reconfiguration and Evolving Regularization Meta Reinforcement Learning (ARER-MetaRL). The framework combines task inference from context, modular routing tailored to each task through an Adjudicate Reconfiguration (AR) network, and Evolving Gradient Regularization (ER) to support rapid adaptation. Tasks differ in the identities and locking angles of the faulty joints, the number of simultaneous faults, the commanded motion direction, and the terrain. Experiments in NVIDIA Isaac Sim cover all 12 actuated joints, with 0–3 stuck joints during meta-training and severity tests involving up to 6 simultaneously stuck joints. ARER-MetaRL achieves higher Mean Episode Linear Velocity Tracking Reward (MELV) and Mean Episode Roll–Pitch Tracking Reward (MERP) than single-task reinforcement learning, ablated variants, and the evaluated learning and adaptation baselines. Cumulative rewards further demonstrate the benefit of combining the proposed components. In the study of fault severity, ARER-MetaRL achieves MELV/MERP values of 0.86/0.84 with one stuck joint and 0.55/0.54 with six stuck joints, compared with 0.21/0.18 and 0.05/0.03, respectively, for the single-task baseline. These results show improved adaptation across different configurations of locked joints and terrain conditions.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia",
    "School of Electrical Engineering, Shandong Huayu University of Technology, Dezhou, China",
    "Department of Naval Architecture and Ocean Engineering, Pusan National University, Busan, South Korea"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Likai Wu"
    },
    {
      "affs": [
        1
      ],
      "name": "Chee-Onn Chow"
    },
    {
      "affs": [
        2
      ],
      "name": "Wei Ru Wong"
    },
    {
      "name": "Joon Huang Chuah"
    },
    {
      "name": "Jeevan Kanesan"
    },
    {
      "name": "Meina Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1egws9102k0wq.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0361099",
  "fulltext_url": null,
  "id": "1egws9102k0wq",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0361099",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2204|Robotic Locomotion and Control"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Musculoskeletal system/Skeleton/Skeletal joints"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive neuroscience/Motor reactions/Postural control"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive neuroscience/Motor reactions/Postural control"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Biological locomotion"
    ],
    [
      "plos",
      "/Earth sciences/Geophysics/Seismology"
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    [
      "plos",
      "/Engineering and technology/Electrical engineering/Electrical faults"
    ],
    [
      "plos",
      "/Engineering and technology/Mechanical engineering/Actuators"
    ],
    [
      "plos",
      "/Engineering and technology/Mechanical engineering/Robotics/Robots"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Musculoskeletal system/Skeleton/Skeletal joints"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Damage mechanics"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Geophysics/Seismology"
    ]
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0361099&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/1egws9102k0wq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0361099",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0361099"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:cognitive neuroscience",
      "subfield": "neuroscience"
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    {
      "field": "earth",
      "origin": "plos:seismology",
      "subfield": "geophysics"
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    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Meta reinforcement learning for robust quadruped locomotion under joint failures",
  "title_text": "Meta reinforcement learning for robust quadruped locomotion under joint failures",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:23Z",
  "url": "https://research.draconitic.com/paper/1egws9102k0wq",
  "venue": "PLOS One",
  "versions": []
}