基于强化学习的高尔夫球摆调整框架,包括时间节奏和运动稳定性
Dong-Jun Lee1, Young-Been Noh1, Jeongeun Byun2
1Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, Incheon 22012, Republic of Korea.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了高尔夫球摆正的强化学习框架,重点关注时间节奏和稳定性. 人工智能生成的校正比以前的方法更流,更协调,增强动态关节运动.
科学领域:
- 体育 生物力学 运动 生物力学
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 现有的高尔夫球摇摆分析往往错过了关键的时间动态,如节奏和速度过渡.
- 框架相似的姿势相似性未能捕捉到有效的摆动纠正所必需的协调,全身运动.
研究的目的:
- 开发一种强化学习 (RL) 框架,用于生成框架级高尔夫球摆正.
- 将时间节奏和生物力学稳定性整合到自动摇摆分析和校正中.
主要方法:
- 制定了高尔夫球摆正作为一个使用近接政策优化 (PPO) 优化的顺序决策问题.
- 设计了一个多期奖励功能,包括姿势准确性,增量改进,稳定性和节奏 (速度-DTW).
主要成果:
- 与静态姿势基线相比,基于RL的方法产生了更平滑,时间连贯的校正.
- 节奏意识奖励显著改善了动态关节运动 (手腕,肩膀),同时保持了下半身的稳定性.
- 修正后的轨迹与专家在空间对齐和时间的模式相匹配.
结论:
- 在RL中明确纳入时间节奏对于现实的和有效的高尔夫球摆正至关重要.
- 拟议的框架为动态体育运动中的自动化专家级教练系统提供了基础.
- 这种方法通过考虑运动的全部时间序列来增强生物力学分析.
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