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

摘要

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.