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迈向人机交互中机器人自主水平的框架。

Toward a framework for levels of robot autonomy in human-robot interaction.

作者信息

Beer Jenay M, Fisk Arthur D, Rogers Wendy A

机构信息

School of Psychology, Georgia Institute of Technology.

出版信息

J Hum Robot Interact. 2014 Jul;3(2):74-99. doi: 10.5898/JHRI.3.2.Beer.

Abstract

A critical construct related to human-robot interaction (HRI) is autonomy, which varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots may interact with one another. Thus, there is a need to understand HRI by identifying variables that influence - and are influenced by - robot autonomy. Our overarching goal is to develop a framework for levels of robot autonomy in HRI. To reach this goal, the framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, the framework proposes a process for determining a robot's autonomy level, by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as guidelines to determine autonomy, categorize the LORA along a qualitative taxonomy, and consider which HRI variables (e.g., acceptance, situation awareness, reliability) may be influenced by the LORA.

摘要

与人机交互(HRI)相关的一个关键概念是自主性,它在不同的机器人平台上差异很大。机器人自主性水平(LORA)从远程操作到完全自主系统不等,影响着人类与机器人相互作用的方式。因此,有必要通过识别影响机器人自主性以及受机器人自主性影响的变量来理解人机交互。我们的总体目标是开发一个用于人机交互中机器人自主性水平的框架。为实现这一目标,该框架在人机交互与人类 - 自动化交互之间建立联系,人类 - 自动化交互是一个有着研究和理解人类相关变量悠久历史的领域。自主性这一概念在人机交互的背景下进行了审视和重新定义。此外,该框架提出了一个通过沿10分分类法对自主性进行分类来确定机器人自主性水平的过程。该框架旨在作为确定自主性的指南,沿着定性分类法对机器人自主性水平进行分类,并考虑哪些人机交互变量(如接受度、态势感知、可靠性)可能受到机器人自主性水平的影响。

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