Simulation of Adaptive Running with Flexible Sports Prosthesis using Reinforcement Learning of Hybrid-link System

arXiv cs.RO / 4/13/2026

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Key Points

片脚（膝下）切断者向けの葉ばね型スポーツ用義足を対象に、義足の柔軟変形と人体運動の相互作用を含む適応的な走行シミュレーション手法を提案しています。
ハイブリッドリンク系にPiece-wise Constant Strainモデルを組み込み、義足の柔軟な変形を反映しながら全身ダイナミクス解析を可能にしています。
リインフォースメント学習ベースのアプローチで、モーションキャプチャからの模倣学習と義足ダイナミクス計算を組み合わせて、走行モーションを生成します。
仮想義足の剛性条件を変えて走行をシミュレーションし、代謝コスト（cost of transport）への影響を解析することで、剛性が走行性能に与える影響を示しています。
実条件から外れた仮想条件下でもシミュレーションと解析に活用できる可能性を示唆しています。

Abstract

This study proposes a reinforcement learning-based adaptive running motion simulation for a unilateral transtibial amputee with the flexibility of a leaf-spring-type sports prosthesis using hybrid-link system. The design and selection of sports prostheses often rely on trial and error. A comprehensive whole-body dynamics analysis that considers the interaction between human motion and prosthetic deformation could provide valuable insights for user-specific design and selection. The hybrid-link system facilitates whole-body dynamics analysis by incorporating the Piece-wise Constant Strain model to represent the flexible deformation of the prosthesis. Based on this system, the simulation methodology generates whole-body dynamic motions of a unilateral transtibial amputee through a reinforcement learning-based approach, which combines imitation learning from motion capture data with accurate prosthetic dynamics computation. 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. Our findings demonstrate the potential of this approach for simulation and analysis under virtual conditions that differ from real conditions.