人类机器人协作系统的多模式数据集:实验数据
Shakra Mehak1,2, Aayush Jain2,3, John D Kelleher4
1Pilz Ireland, Cork Campus, Ireland.
Data in brief
|December 1, 2025
概括
本研究引入了一套新的多式联网数据集,用于评估人类在人机交互 (HRI) 中使用示范编程 (PbD) 的人类表现. 这些数据有助于开发先进的HRI系统和改进机器人学习过程.
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:
- 人机交互 (HRI) 在协作环境中至关重要.
- 通过演示编程 (PbD) 评估人类表现是具有挑战性的,因为数据有限.
研究的目的:
- 呈现一个全面的多式联运数据集,用于评估人类在HRI中的表现.
- 评估PbD方法在增强机器人学习方面的有效性.
主要方法:
- 收集客观 (机器人/人类轨迹,眼球追踪) 和主观 (NASA-TLX,SUS) 的数据.
- 使用了UR10e机器人,运动跟踪,眼睛跟踪眼镜和问卷.
- 在四个反条件下,对两个动感机器人教学任务进行了28名参与者的实验.
主要成果:
- 数据集包括编程效率,认知工作量,可用性和人体工程学因素.
- 数据包括机器人/人类运动,眼睛跟踪指标和主观参与者反.
- 实验设置涉及用于机器人教学任务的人机界面 (HMI).
结论:
- 该数据集推进了人机教学互动,并支持自适应性HRI系统的开发.
- 它作为改善PbD算法和训练HRI机器学习模型的资源.
- 为机器人学习和协作提供安全和人体工程学的进一步研究提供便利.
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