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

TL;DR

This paper presents a reinforcement learning-based simulation method for adaptive running with a flexible sports prosthesis, enabling analysis of different stiffness conditions and their impact on performance.

Contribution

It introduces a hybrid-link system with a Piece-wise Constant Strain model for prosthetic flexibility and combines imitation learning with dynamics computation for whole-body simulation.

Findings

Simulated running motions under various prosthetic stiffness conditions.

Analyzed the impact of prosthetic stiffness on metabolic cost and performance.

Demonstrated potential for virtual testing of prosthetic designs.

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.