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首页> 外文期刊>The International journal of robotics research >Improving user specifications for robot behavior through active preference learning: Framework and evaluation
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Improving user specifications for robot behavior through active preference learning: Framework and evaluation

机译：通过活动偏好学习改进机器人行为的用户规范：框架和评估

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摘要

An important challenge in human-robot interaction (HRI) is enabling non-expert users to specify complex tasks for autonomous robots. Recently, active preference learning has been applied in HRI to interactively shape a robot's behavior. We study a framework where users specify constraints on allowable robot movements on a graphical interface, yielding a robot task specification. However, users may not be able to accurately assess the impact of such constraints on the performance of a robot. Thus, we revise the specification by iteratively presenting users with alternative solutions where some constraints might be violated, and learn about the importance of the constraints from the users' choices between these alternatives. We demonstrate our framework in a user study with a material transport task in an industrial facility. We show that nearly all users accept alternative solutions and thus obtain a revised specification through the learning process, and that the revision leads to a substantial improvement in robot performance. Further, the learning process reduces the variances between the specifications from different users and, thus, makes the specifications more similar. As a result, the users whose initial specifications had the largest impact on performance benefit the most from the interactive learning.
机译：人机交互（HRI）中的一个重要挑战是使非专家用户指定自主机器人的复杂任务。最近，活跃的偏好学习已经应用于HRI以交互方式塑造机器人的行为。我们研究了一个框架，其中用户在图形界面上指定允许的机器人运动的约束，产生机器人任务规范。但是，用户可能无法准确评估这种限制对机器人性能的影响。因此，我们通过迭代地呈现具有替代解决方案的用户来修改规范，其中可能会违反一些约束，并了解这些替代方案之间的用户选择的约束的重要性。我们展示了在用户学习中的框架，在工业设施中的材料运输任务。我们表明，几乎所有用户都接受替代解决方案，从而通过学习过程获得修订规范，并且修订导致机器人性能的显着提高。此外，学习过程减少了来自不同用户的规格之间的差异，因此使得规范更加相似。因此，初始规范对绩效的影响最大的用户受益于互动学习中最大的影响。

著录项

  • 来源
    《The International journal of robotics research》 |2020年第6期|651-667|共17页
  • 作者

    Nils Wilde; Alexandra Blidaru; Stephen L Smith; Dana Kulic;

  • 作者单位

    Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada;

    Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada;

    Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada;

    Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Monash University Melbourne Victoria Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cognitive HRI; motion planning;

    机译：认知HRI;运动计划;

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