一个路线图,通过人类机器人应用程序的协作智能标准来提高数据质量
Shakra Mehak1,2, Inês F Ramos3, Keerthi Sagar4
1Pilz Ireland Industrial Automation, Cork, Ireland.
Frontiers in robotics and AI
|December 27, 2024
概括
确保数据质量对于安全关键的协作情报 (CI) 系统至关重要. 本研究针对工业应用的人机交互 (HRI) 的数据质量挑战.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 协作智能 (CI) 系统对安全至关重要,依靠可靠的人机交互来防止伤害.
- 越来越多的CI应用程序的数据驱动性要求高质量的数据,以在不可预测的环境中提供稳健的性能.
- 遵守数据质量标准对于在工业环境中推进CI系统至关重要.
研究的目的:
- 识别和解决工业CI应用中的数据质量挑战,特别是在人机交互 (HRI) 中.
- 介绍两个用例,展示HRI中的数据收集和分析,以提高CI系统可靠性.
- 为多模式HRI数据采集提出混合标准化方法.
主要方法:
- 在自然主义机器人学习场景中量化人类和机器人的表现的框架的开发.
- 实现适应式多式联络远程操作系统的实时用户状态监控.
- 从现有的ISO数据质量标准推导出混合标准化方法.
主要成果:
- 该研究强调了在工业HRI数据收集中遇到的具体数据质量挑战.
- 使用案例展示了收集和使用HRI数据以提高CI系统的适应性和性能的实际方法.
- 提出了一个新的混合标准化框架来管理多式联通HRI数据质量.
结论:
- 解决数据质量问题对于工业中CI系统的安全和有效部署至关重要.
- 提出的用例和拟议的标准化为改善HRI数据采集和管理提供了有价值的见解.
- 这些发现通过更好的数据质量实践,有助于推进安全关键的CI应用.
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