优化人机交接:适应式运输方法的影响
Marco Käppler1, Ilshat Mamaev2, Hosam Alagi2
1Institute of Human and Industrial Engineering (ifab), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
Frontiers in robotics and AI
|July 31, 2023
概括
适应性机器人物体运输方法并没有显著增加交付时间,但与非适应性方法相比,降低了信任和安全感知. 这两种方法随着时间的推移而得到了改进,在人机交互中显示了运动学习.
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
- 运动学习是指运动学习.
背景情况:
- 机器人越来越多地在物体交付期间与人类进行物理交互.
- 机器人行动的可预测性对于流的人机器人协作至关重要.
- 适应性机器人行为旨在提高交付效率和人类舒适度.
研究的目的:
- 为了比较一个自适应的机器人物体运输方法与一个非自适应的人机器人交接的方法.
- 评估适应性对交付时间,人类干预和主观评级的影响.
- 研究机器学习在人机物体交换中的作用.
主要方法:
- 一个带有先进传感器的Franka Panda机器人臂被用于物体运输.
- 40名受试者每人进行了40次交接,比较了适应性和非适应性运输.
- 关键指标包括物理交付时间,早期干预以及主观的信任/安全感知.
主要成果:
- 与非自适应运输相比,自适应运输并没有显著增加交付时间.
- 非适应性运输并没有导致更早的人类干预.
- 受试者报告说,在适应性方法下,信任和安全感受较低.
结论:
- 虽然自适应式运输并没有阻碍效率,但它却对用户的信任产生了负面影响.
- 这两种方法都显示出显著的学习效应,减少了连续试验的交付时间.
- 了解机器学习对于开发以用户为中心的工业应用机器人系统至关重要.
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