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Review and Preview01:10

Review and Preview

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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The history of therapeutic communication can be traced back to Florence Nightingale, who emphasized the importance of developing trusting relationships with patients. She taught that the presence of nurses with patients results in therapeutic healing.
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Compared to ionic bonds, which results from the transfer of electrons between metallic and nonmetallic atoms, covalent bonds result from the mutual attraction of atoms for a “shared” pair of electrons.
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System Transparency in Shared Autonomy: A Mini Review.

Victoria Alonso1, Paloma de la Puente2,3

  • 1ETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Madrid, Spain.

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|December 18, 2018
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Summary
This summary is machine-generated.

Transparency in shared autonomy means understanding robot actions and intentions. This review clarifies its definitions and methods, crucial for improving human-robot interaction and trust.

Keywords:
communicationhuman-robot interactioninterfaceobservabilitypredictabilityshared autonomytransparencyuser-centered design

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Area of Science:

  • Human-Robot Interaction
  • Robotics
  • Artificial Intelligence

Background:

  • Transparency is key in shared autonomy for effective human-robot collaboration.
  • Existing literature offers various definitions and methods for achieving system transparency.
  • Understanding system behavior, its causes, and future actions is central to transparency.

Purpose of the Study:

  • To review definitions and methods for improving transparency in shared autonomy.
  • To clarify the role of transparency across different interaction requirements and autonomy levels.
  • To identify research gaps and future directions in transparency for human-robot systems.

Main Methods:

  • Literature review of transparency definitions and applications in human-robot interaction.
  • Analysis of methods to enhance system observability and predictability.
  • Examination of mechanical transparency and its impact on user trust and performance.

Main Results:

  • Transparency encompasses observability, predictability, and understanding of system actions and intentions.
  • Interface design, training, and natural language explanations are common methods to enhance transparency.
  • Mechanical transparency, where robots follow human movements without resistance, builds trust and reduces errors.

Conclusions:

  • A clear understanding of transparency is vital for advancing shared autonomy frameworks.
  • Further research is needed to explore diverse applications and autonomy degrees.
  • Transparent human-robot systems are user-centered, enhancing performance and trust.