模拟人类步行:一种基于模型的强化学习方法,使用肌肉骨模型

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Binbin Su¹, Elena M Gutierrez-Farewik^1,2

¹KTH MoveAbility Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.

Frontiers in neurorobotics

|October 30, 2023

概括

这项研究将强化学习与肌肉骨模型相结合,创建了类似人类步行的模型. 该框架显示了模拟病态步行的潜力,并推进了生物机械模拟.

科学领域:

生物力学生物力学
机器人技术机器人技术机器人技术
计算机建模计算建模

背景情况:

强化学习 (RL) 推进了人类运动模拟的控制模型.
生物机械模型对于实现真实的人类运动生成至关重要.
将RL与肌肉骨模型集成,可以提高步态模拟的准确性.

研究的目的:

开发人类行走的控制模式,使用集成的RL和肌肉骨模型.
调查模型在没有参考运动数据的情况下产生自然主义步态的能力.
探索模拟病态步行的潜力.

主要方法:

一个肌肉骨模型 (干部,骨盆,腿) 与RL算法相结合.
模拟在没有目标速度的情况下进行,然后使用强加速度.
马尔科夫决策过程问题是使用协差矩阵适应演化策略来解决的.

主要成果:

该模型自行选择了1.45米/秒的稳定步行速度.
模拟的部和膝盖动力学与实验数据密切匹配.
与部和膝盖相比,脚运动预测的准确性较低.

结论:

集成的RL和肌肉骨模型成功模拟了人类的行走.
该框架显示了预测步行异常的潜力,例如来自肌肉衰弱的异常.
这种方法推动了复杂的人类运动模型的开发.

关键词:

在CMA-ES中,CMA-ES是指CMA.人类和人形运动分析.动力学是动力学.运动合成运动合成最好的控制和控制是最优的.优化的优化优化优化.基于反射的控制是基于反射的控制

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