Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modeling.

ABSTRACT:

Introduction

Recent advancements in reinforcement learning algorithms have accelerated the development of control models with high-dimensional inputs and outputs that can reproduce human movement. However, the produced motion tends to be less human-like if algorithms do not involve a biomechanical human model that accounts for skeletal and muscle-tendon properties and geometry. In this study, we have integrated a reinforcement learning algorithm and a musculoskeletal model including trunk, pelvis, and leg segments to develop control modes that drive the model to walk.

Methods

We simulated human walking first without imposing target walking speed, in which the model was allowed to settle on a stable walking speed itself, which was 1.45 m/s. A range of other spee

SUBMITTER: Su B 

PROVIDER: S-EPMC10601656 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature