{"paper":{"title":"Simulation of Adaptive Running with Flexible Sports Prosthesis using Reinforcement Learning of Hybrid-link System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A reinforcement learning system with a hybrid-link model simulates amputee running motions and shows that prosthetic stiffness changes metabolic energy cost.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Ko Yamamoto, Yuta Shimane","submitted_at":"2026-04-10T02:44:29Z","abstract_excerpt":"This study proposes a reinforcement learning-based framework for adaptive running motion simulation in a unilateral transtibial amputee using a hybrid-link system that incorporates the flexibility of a leaf-spring-type sports prosthesis. The design and selection of sports prostheses typically rely on trial and error. A comprehensive whole-body dynamics analysis that accounts for interactions between human motion and prosthetic deformation can provide valuable insights for user-specific design and selection. The proposed hybrid-link system enables such analysis by integrating a Piece-wise Const"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We simulated running motions under different virtual prosthetic stiffness conditions and analyzed the metabolic cost of transport obtained from the simulations, suggesting that variations in stiffness influence running performance.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The hybrid-link system with the Piece-wise Constant Strain model, combined with imitation learning from motion capture, produces whole-body dynamics that are sufficiently realistic to support conclusions about how real prosthetic stiffness affects metabolic cost and performance.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Reinforcement learning on a hybrid-link model of a flexible sports prosthesis generates simulated running motions for amputees and indicates that prosthesis stiffness affects metabolic cost and performance.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A reinforcement learning system with a hybrid-link model simulates amputee running motions and shows that prosthetic stiffness changes metabolic energy cost.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"36ad30ea0a10e1facb89332f0547183409d4d31b56697a72c20270f7f24f19c9"},"source":{"id":"2604.08882","kind":"arxiv","version":2},"verdict":{"id":"011e0d43-5e60-4bb3-9576-8d1e063a0ca4","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T18:19:56.799791Z","strongest_claim":"We simulated running motions under different virtual prosthetic stiffness conditions and analyzed the metabolic cost of transport obtained from the simulations, suggesting that variations in stiffness influence running performance.","one_line_summary":"Reinforcement learning on a hybrid-link model of a flexible sports prosthesis generates simulated running motions for amputees and indicates that prosthesis stiffness affects metabolic cost and performance.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The hybrid-link system with the Piece-wise Constant Strain model, combined with imitation learning from motion capture, produces whole-body dynamics that are sufficiently realistic to support conclusions about how real prosthetic stiffness affects metabolic cost and performance.","pith_extraction_headline":"A reinforcement learning system with a hybrid-link model simulates amputee running motions and shows that prosthetic stiffness changes metabolic energy cost."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.08882/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}