强大的上肢运动学习和概括使用层次神经机械控制模型与深度强化学习和肌肉协同作用
IEEE journal of biomedical and health informatics
|November 28, 2025
概括
这项研究为生物灵感机器人引入了一种新的层次神经机械控制模型 (HNCM). HNCM增强了运动学习,强度,并产生类似人类的运动,超越现有方法.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物模拟学是一种生物模拟学.
- 计算神经科学是一种神经科学.
背景情况:
- 生物灵感机器人提供了优势,但在高效,类似人类的运动学习方面遇到了困难.
- 神经机械机制是模拟生物运动控制和技能获取的关键.
研究的目的:
- 为先进的机器人运动开发一个仿生等级神经机械控制模型 (HNCM).
- 为了提高高维的肌肉控制,对干扰的强度和概括能力.
- 为了在机器人中产生自然,类似人类的伸展运动.
主要方法:
- 实施了层次神经机械控制模型 (HNCM),集成了深度强化学习.
- 使用近接政策优化算法对高级控制器 (脊柱上系统仿真).
- 采用双路径自适应低级控制器模拟生物运动路径 (基于协同作用和非基于协同作用).
主要成果:
- 与基线方法相比,HNCM显示出更高的控制精度和学习效率.
- 该模型显示了对未经训练的任务的增强概括性和对噪声干扰的稳定性.
- 生成的运动轨迹更自然和生物学上更合理,类似于人类的伸手模式.
结论:
- 开发的HNCM推进了生物灵感机器人的神经机械控制领域.
- 这个模型为高效,可概括和类似人类的运动学习提供了一个强大的框架.
- 为复杂的肌肉骨机器人技术的未来应用提供了基础.
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