使用动态贝叶斯优化在运动学习的存在下诱导所需效果:一个模拟研究
GilHwan Kim1, Haider Ali Chishty1, Fabrizio Sergi1,2
1Department of Mechanical Engineering, University of Delaware, Newark, DE, USA.
Computer methods in biomechanics and biomedical engineering
|December 3, 2025
概括
动态贝叶斯优化 (DBO) 有效地优化了循环中人控制系统,在涉及机器学习的模拟中表现优于标准贝叶斯优化 (BO). 当收集了足够的数据时,DBO显示出适应性设备控制的希望.
科学领域:
- 机器人和人机交互的人机交互
- 计算神经科学和运动学习
背景情况:
- 人在循环优化 (HILO) 对于自适应设备控制至关重要.
- 机器学习在优化过程中引入了用户输出的动态变化.
- 标准贝叶斯优化 (BO) 可能会在时间变化的用户响应中扎.
研究的目的:
- 为了对设备控制输入的HILO进行动态贝叶斯优化 (DBO) 的评估.
- 为了评估DBO的适用性,当用户输出由于机器学习而改变时.
- 在模拟的自适应控制场景中,将DBO与标准BO进行比较.
主要方法:
- 使用时间依赖参与者反应的模拟.
- 模拟包括运动学习的状态空间模型.
- 对DBO和标准BO收率的比较分析.
主要成果:
- 经过一定的代次数,DBO与标准BO相比,在最佳输入和输出方面表现出优越的趋同.
- DBO的性能优势增加了足够的代来区分学习和变化.
- 模拟证实了DBO在处理动态用户响应方面的有效性.
结论:
- 动态贝叶斯优化 (DBO) 是适应性设备控制中适合HILO的算法.
- 在处理运动学习和动态用户状态时,DBO提供了比标准BO更好的性能.
- 有效地实施DBO需要足够的代来准确评估学习.
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