实时人类进步估计与在线动态时间扭曲协作机器人的机器人技术
Davide De Lazzari1, Matteo Terreran1, Giulio Giacomuzzo1
1Department of Information Engineering, University of Padua, Padua, Italy.
Frontiers in robotics and AI
|December 22, 2025
概括
本研究引入了新的动态时间扭曲 (DTW) 方法,用于实时人类行动进度估计,增强人机协作. 该PACE框架提高了交互流性,并减少了用户研究中的等待时间.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 实时的人类行动进度估计对于有效的人机协作至关重要.
- 目前用于行动进展估计的方法未得到充分探索,限制了无协作.
研究的目的:
- 提出开放式软DTW (OS-DTWEU) 的第一个实时应用.
- 引入OS-DTWWP,这是一个新的DTW变体,用于捕获本地相关性.
- 通过行动完成估计 (PACE) 制定主动协助框架,用于同步的机器人协助.
主要方法:
- 实现了实时的OS-DTWEU,并引入了具有Windowed-Pearson距离的OS-DTWWP.
- 将这些方法集成到PACE框架中,使用强化学习来估计行动的完成.
- 对12名参与者进行了椅子组装任务和用户研究的实验.
主要成果:
- OS-DTWWP在捕捉本地运动模式方面表现出优越性.
- OS-DTWEU在具有一致绝对立场的任务中被证明是有效的.
- 该PACE框架显著改善了交互流性,减少了等待时间,并获得了积极的用户反.
结论:
- 拟议的OS-DTW变体和PACE框架在实时人类行动进度估计方面取得了重大进展.
- 这项研究使人机协作更加流和高效.
- 这些发现突出了适应性机器人协助复杂任务的潜力.
相关概念视频
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