基于联合主动监督自治（JISA）框架的人机交互
Human-Robot Interaction via a Joint-Initiative Supervised Autonomy (JISA) Framework
摘要：
在本文中，我们提出并验证了一个联合主动监督自主（JISA）框架的人机交互（HRI），其中机器人保持其自信（SC）的措施，同时执行任务，只有提示人类主管帮助时，其SC下降。同时，在任务执行期间，人类监督者可以基于他/她的态势感知（SA）来干预正在执行的任务。为了评估JISA的适用性和实用性，它实现了两个不同的HRI任务：基于网格的协同同时定位与地图构建（SLAM）和自动拼图重建。增强现实（AR）（用于SLAM）和二维图形用户界面（GUI）（用于谜题重建）是定制设计的，以增强人类SA并允许人类和代理之间的直观交互。实验证明了JISA框架的优越性。在SLAM中，JISA生成的高级地图不需要对任何SLAM库存地图进行后期处理;此外，与传统方法相比，JISA将所需的映射时间减少了约50%。在自动化拼图重建中，JISA框架的性能优于完全自主的解决方案，以及由代理人提示的按需人工干预产生的解决方案。
英文摘要：
In this paper, we propose and validate a Joint-Initiative Supervised Autonomy (JISA) framework for Human-Robot Interaction (HRI), in which a robot maintains a measure of its self-confidence (SC) while performing a task, and only prompts the human supervisor for help when its SC drops. At the same time, during task execution, a human supervisor can intervene in the task being performed, based on his/her Situation Awareness (SA). To evaluate the applicability and utility of JISA, it is implemented on two different HRI tasks: grid-based collaborative simultaneous localization and mapping (SLAM) and automated jigsaw puzzle reconstruction. Augmented Reality (AR) (for SLAM) and two-dimensional graphical user interfaces (GUI) (for puzzle reconstruction) are custom-designed to enhance human SA and allow intuitive interaction between the human and the agent. The superiority of the JISA framework is demonstrated in experiments. In SLAM, the superior maps produced by JISA preclude the need for post processing of any SLAM stock maps; furthermore, JISA reduces the required mapping time by approximately 50 percent versus traditional approaches. In automated puzzle reconstruction, the JISA framework outperforms both fully autonomous solutions, as well as those resulting from on-demand human intervention prompted by the agent.
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。
