通过深度强化学习学习的基于合成运动克隆来实现跨度臂的运动预测
Muhammad Hannan Ahmed1, Kyo Kutsuzawa1, Mitsuhiro Hayashibe1
1Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.
Biomimetics (Basel, Switzerland)
|August 25, 2023
概括
本研究介绍了一个深度强化学习框架,用于生成合成手臂运动数据,用于训练人工神经网络 (ANN). 这些克隆数据有效地训练ANN进行假肢手臂控制,改善了跨体截肢者的肘部运动预测.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 生物医学工程 生物医学工程
- 人与计算机的交互
背景情况:
- 截肢者面临着直观的假肢手臂控制的挑战,特别是达到任务.
- 人工神经网络 (ANN) 显示出对自动化假肢手臂运动的承诺,如肘部关节运动和手腕伸展-伸缩.
- 培训ANN需要广泛的人类运动数据在各种活动和条件,这往往很难收集.
研究的目的:
- 建议使用深度强化学习 (DRL) 来克隆运动数据集的新框架,以解决假肢手臂控制中的数据稀缺问题.
- 为了生成合成运动数据,准确地模仿现实世界的手臂达到任务.
- 评估克隆运动数据在训练ANN预测自然假肢手臂运动方面的有效性.
主要方法:
- 收集了六个人从执行多方向伸手任务时的真实手臂运动数据.
- 利用物理模拟和基于DRL的手臂操纵来生成合成运动数据.
- 训练了一个CNN-LSTM网络,使用真实,DRL生成和混合数据集来评估性能.
主要成果:
- 克隆的运动数据有效地训练了ANN,在多个受试者中准确预测自然肘部运动.
- 使用结合真实和克隆数据集的数据增强通过多样化有限的培训数据来增强ANN的稳定性.
- 基于DRL的框架成功地模仿了人类类似的协同运动来操纵手臂.
结论:
- 拟议的DRL框架有效地生成合成运动数据集,用于假肢手臂控制.
- 合成数据增强显著提高了用于假肢应用的ANN的准确性和稳定性.
- 这种方法对创建合成数据集和推进自动化假肢肘部运动控制具有广泛的影响.
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