{
  "abstract": "In consecutive upper-limb motor coordination tasks, where a new condition is imposed once a subgoal is achieved, kinematic demonstrations reveal what a human does but not how cautiously, so existing imitation learning misses the adaptation to the new condition. To close this gap, we propose designing reward and constraint functions from biopotential-informed characteristics of human surface electromyography (sEMG). The task begins as a Markov decision process (MDP) and turns into a constrained MDP (cMDP) once the first subtask is completed. The sEMG data are decomposed into shared and task-specific muscle synergies; the former infers a quadratic-regulator-formed reward by inverse optimal control, and the latter defines a subtlety coefficient bounding the action magnitude in the constrained phase. A two-holes two-balls patience cube exemplifies the task, and the policy is trained by constrained policy optimization and deployed on a robot manipulator. Whereas unconstrained PPO and SAC agents complete the constrained phase in only 1% of the episodes, the proposed agent completes 100% with the shortest completion time (1.76 s) among four constrained variants and most closely reproduces the human’s dwell-time distribution and zone transitions near the hole, yielding a feasible and human-like policy.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Yonsei University, Wonju 26493, Republic of Korea",
    "Human Assistive Neurorobotics Laboratory, Yonsei University, Wonju 26493, Republic of Korea",
    "Department of Software, Yonsei University, Wonju 26493, Republic of Korea"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ho-Jin Jung",
      "orcid": "0000-0002-5509-2083"
    },
    {
      "affs": [
        1
      ],
      "name": "Jang-Hyeon Kim",
      "orcid": "0000-0002-7327-9706"
    },
    {
      "affs": [
        2
      ],
      "name": "Han-Ul Yoon",
      "orcid": "0000-0001-8870-3103"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/59h4t7nkd70dt.bib",
  "cited_by": 0,
  "doi": "10.3390/math14203718",
  "fulltext_url": null,
  "id": "59h4t7nkd70dt",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3718",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Reinforcement Learning in Robotics"
    ]
  ],
  "openalex": "W7221610101",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/59h4t7nkd70dt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203718",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3718"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Biopotential-Informed Reward Inference and Constraint Design for Consecutive Upper-Limb Motor Coordination Tasks in Robot Manipulation",
  "title_text": "Biopotential-Informed Reward Inference and Constraint Design for Consecutive Upper-Limb Motor Coordination Tasks in Robot Manipulation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/59h4t7nkd70dt",
  "venue": "Mathematics",
  "versions": []
}