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基于肌肉力量模型和深度增强策略的运动相互作用控制.
Hongyan Liu1, Hanwen Zhang1, Junghee Lee1
1Department of Marine Convergence Design Engineering, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea.
Biomimetics (Basel, Switzerland)
|March 27, 2024
概括
这项研究介绍了一种使用肌肉力量和深度强化学习的先进人体运动控制模型. 新型号显著提高了复杂环境中的运动真实性和自适应控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物力学 生物力学
- 人工智能的人工智能
背景情况:
- 当前的运动交互模型缺乏足够的忠实性和适应复杂环境的适应性.
- 现有的模型与现实世界的动态和不可预测的场景作斗争.
研究的目的:
- 开发一种具有增强忠实性和自我适应性的人类运动控制模型.
- 在复杂的环境中改进自主决策和适应性控制.
主要方法:
- 构建了一个人运动控制模型,整合了一个肌肉力模型和一个阶段粒子群.
- 使用深度决定性的梯度策略算法来控制运动相互作用.
- 使用深度强化学习来实现适应性控制策略.
主要成果:
- 实现了高关节轨迹相关性 (高达0.90) 和肌肉活动相关性 (高达0.84).
- 在混合障碍环境中证明有效的自主决策和适应性控制.
- 在1.1 × 10^3次训练代后,在步行距离 (423米) 中表现优于比较模型.
结论:
- 拟议的模型提供卓越的运动保真性和环境适应性.
- 这种方法为智能运动交互和控制提供了理论基础.
- 该模型可以在复杂,动态的环境中实现自主决策.
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