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Informing Real-Time Corrections in Corrective Shared Autonomy Through Expert Demonstrations
IEEE Robotics and Automation Letters ( IF 5.2 ) Pub Date : 2021-07-02 , DOI: 10.1109/lra.2021.3094480
Michael Hagenow , Emmanuel Senft , Robert Radwin , Michael Gleicher , Bilge Mutlu , Michael Zinn

C orrective Shared Autonomy is a method where human corrections are layered on top of an otherwise autonomous robot behavior. Specifically, a Corrective Shared Autonomy system leverages an external controller to allow corrections across a range of task variables (e.g., spinning speed of a tool, applied force, path) to address the specific needs of a task. However, this inherent flexibility makes the choice of what corrections to allow at any given instant difficult to determine. This choice of corrections includes determining appropriate robot state variables, scaling for these variables, and a way to allow a user to specify the corrections in an intuitive manner. This letter enables efficient Corrective Shared Autonomy by providing an automated solution based on Learning from Demonstration to both extract the nominal behavior and address these core problems. Our evaluation shows that this solution enables users to successfully complete a surface cleaning task, identifies different strategies users employed in applying corrections, and points to future improvements for our solution.

中文翻译:

通过专家演示在纠正性共享自治中通知实时更正

纠正共享自主是一种方法,其中人工纠正是在其他自主的机器人行为之上分层的。具体而言,修正共享自主系统利用外部控制器允许跨一系列任务变量(例如工具的旋转速度、施加的力、路径)进行修正,以满足任务的特定需求。然而,这种固有的灵活性使得难以确定在任何给定时刻允许哪些修正的选择。这种修正选择包括确定适当的机器人状态变量、对这些变量进行缩放,以及允许用户以直观方式指定修正的方式。这封信通过提供基于从演示中学习的自动化解决方案来提取名义行为并解决这些核心问题,从而实现高效的纠正共享自治。我们的评估表明,该解决方案使用户能够成功完成表面清洁任务,确定用户在应用修正时采用的不同策略,并指出我们解决方案的未来改进。
更新日期:2021-07-20
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