模型预测游戏控制用于个性化和有针对性的交互式辅助
Abdelwaheb Hafs1, Anaïs Farr2, Dorian Verdel3
1Université Paris-Saclay, Inria, CIAMS, Gif-sur-Yvette, France. abdelwaheb.hafs@universite-paris-saclay.fr.
Communications engineering
|February 24, 2026
概括
这项研究介绍了一种用于接触机器人的新型控制器,该控制器可以预测人类的运动,从而实现更顺,更有效的协作. 新系统增强了人机交互,减少了用户的劳动力,并使可适应的机器人协助培训和康复.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与机器人的交互
- 控制理论 控制理论
背景情况:
- 接触机器人在体育训练和制造中至关重要.
- 当前的控制方法优先考虑系统性能,而不是人类用户的意图.
- 优化人机协作需要考虑人类的控制策略.
研究的目的:
- 开发一种用于接触机器人的新型控制器,可以预测并结合人类运动控制.
- 通过使用模型预测游戏 (MPG) 控制器来调查人机协同适应.
- 评估协助元参数对交互动态的影响.
主要方法:
- 开发了一个基于游戏的差异控制器 (MPG控制器) 来预测人类运动控制.
- 进行了实验来分析人类和机器人的共同适应.
- 使用辅助元参数来调节机器人的辅助级别.
主要成果:
- MPG控制器确保了稳定的人机交互,同时减少了人类的体力劳动.
- 机器人成功地适应了个别的人类交互行为.
- 人类适应了机器人的策略,他们的行为被辅助元参数调节.
- 互动与游戏理论原则相一致,证明人类对机器人的战略的理解.
结论:
- 通过预测人类的意图,MPG控制器促进了最佳的人机交互.
- 人机协同适应是有效协作任务的关键因素.
- 辅助元参数提供了一种通用方法,用于在机器人辅助的培训和康复应用中指导人类行为.
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