通过专家论证在纠正共享自治中通知实时的纠正
Informing Real-time Corrections in Corrective Shared Autonomy Through Expert Demonstrations
摘要：
纠正共享自治是一种方法，其中人类纠正是分层的，否则自主机器人行为的顶部。具体地，校正共享自主系统利用外部控制器来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）以解决任务的特定需要。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何种修正。校正的这种选择包括确定适当的机器人状态变量、这些变量的缩放以及允许用户以直观的方式指定校正的方式。本文通过提供一种基于示教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地完成表面清洁任务，识别用户在应用校正时采用的不同策略，并指出我们解决方案的未来改进。
英文摘要：
Corrective 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 paper 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
