Predicting Human Locomotion in Reduced Gravity via Deep Learning-Driven Musculoskeletal Simulation

Abstract

Understanding human walking locomotion in reduced gravity could enhance astronaut mobility and improve the space exploration efficiency. However, existing studies often require high costs and significant time and resource commitments for natural locomotion studies. Here, we present a deep reinforcement learning (DRL)-based simulation framework that predicts locomotion patterns across reduced-gravity environments by learning control policies tailored to each gravity condition. This approach identifies optimal gait behaviors without extensive experimental data and can be extended to include assistive devices such as exoskeletons, enabling systematic studies of human-exoskeleton interaction and walking adaptation in reduced-gravity settings. To validate the simulation, we utilized a mechanical body-weight suspension system to replicate reduced gravity and conducted walking experiments under three reduced gravity levels. The stance phase (ST) decreased from 72.59% to 61.03% and the swing phase (SW) increased from 27.41% to 38.97%, with stride duration nearly constant. Under the exoskeleton assistance, ST decreased from 63.52% to 62.02%, and SW increased from 36.48% to 37.98%. Hip joint range of motion decreased consistently with gravity in both conditions. These trends closely matched experimental results, demonstrating the potential of DRL-based simulations for studying locomotion and assistive strategies in reduced gravity.