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相关概念视频

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

1.6K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
308
Machines: Problem Solving I01:22

Machines: Problem Solving I

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
319
Naturalistic Observations02:30

Naturalistic Observations

15.4K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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相关实验视频

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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在对象组织任务中使用社会机器人的循环内人类错误检测.

Helena Anna Frijns1, Matthias Hirschmanner2, Barbara Sienkiewicz3

  • 1Institute of Management Science, TU Wien, Vienna, Austria.

Frontiers in robotics and AI
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概括

了解机器人故障是改善人机合作的关键. 参与者测试系统限制表明,故障可以有效地提高用户对机器人系统的心理模型.

关键词:
这是一个错误的错误错误的错误.失败的失败失败的失败失败的失败人与机器人的交互人机交互设计的人机交互设计多式联网接口 多式联网接口透明度 透明度 透明度

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Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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科学领域:

  • 人与机器人的交互
  • 机器人技术 机器人技术 机器人技术
  • 认知科学 认知科学

背景情况:

  • 在人机协作中,失败是不可避免的,需要强大的错误检测和补救策略.
  • 了解系统意识错误,特别是机器人的知识库,对于设计弹性机器人系统至关重要.
  • 人类合作伙伴可以通过深入了解机器人的知识和决策过程来增强错误检测.

研究的目的:

  • 在联合对象组织任务期间调查人机交互中的故障.
  • 探索不同沟通方式 (语音,可视化,组合) 对错误检测和用户心理模型的影响.
  • 分析用户如何与机器人系统互动,并测试机器人系统的局限性,从而引发错误.

主要方法:

  • 对31名参与者进行了用户研究,他们与Pepper机器人进行了对象组织任务的交互.
  • 采用了各种各样的沟通方式:语音,可视化和两者的组合,使机器人能够传达学习的配置.
  • 通过观察,采访和分析生成的对象配置来收集数据,以了解错误模式和用户行为.

主要成果:

  • 在31名参与者中,有23人更喜欢语音和可视化结合的交流方式.
  • 参与者故意增加任务的复杂性,探测系统的局限性和引发错误.
  • 观察到的试错行为,表明故障源于机器人能力,用户操作和环境相互作用的相互作用.

结论:

  • 结合通信模式显著提高了用户对人机交互的偏好.
  • 由测试系统边界驱动的用户诱导的故障,在完善理解方面起着富有成效的作用.
  • 人机协作中的失败可以被利用来构建更准确的机器人系统的用户心理模型,并改善整体交互设计.