Simulated Bipedal Exoskeleton Gait Learning Using Reinforcement Learning Techniques

doi:10.12680/balneo.2025.942

2025-12-01

PRE

Mihaela Rusanovschi *

DOI：10.12680/balneo.2025.942

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摘要

En 中文

本文介绍了一种强化学习（RL）方法，作为训练双足下肢外骨骼计算机仿真模型行走的技术，无需显式监督。开发了一个具有六个关节（左、右髋、膝和踝关节）的双足外骨骼仿真模型，包括用于训练RL智能体的近端策略优化算法的实现。该系统使用Python和OpenAI Gym库实现，后者提供了模拟交互和习得运动动力学的环境。RL训练的智能体能够通过与仿真环境的交互和复杂的奖励系统学习稳定运动。结果表明，RL在自适应控制外骨骼方面具有潜力，并为外骨骼控制与训练的进一步研究奠定了基础。

Keyword:

Reinforcement Learning

Exoskeleton

Policy

Agent

State

Action

Reward

期刊

Balneo and PRM Research Journal

IF:

1.1

论文数:

被引数:

299

机构

carol davila university of medicine & pharmacy

学者数:

8.6K

论文数: 4.6K

被引数: 13

Technical University Moldova

学者数:

324

论文数: 256

被引数: 2

引用论文

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