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.

中文翻译：

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通过联合主动监督自治 (JISA) 框架实现人机交互

在本文中，我们提出并验证了人机交互 (HRI) 的联合主动监督自治 (JISA) 框架，其中机器人在执行任务时保持自信心 (SC) 的度量，并且仅提示当其 SC 下降时，人类主管寻求帮助。同时，在任务执行期间，人类主管可以根据他/她的情境意识 (SA) 干预正在执行的任务。为了评估 JISA 的适用性和实用性，它在两个不同的 HRI 任务上实施：基于网格的协同同步定位和映射 (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)（用于拼图重建）是定制设计的，以增强人类 SA 并允许人与代理之间的直观交互。实验证明了 JISA 框架的优越性。在 SLAM 中，由 JISA 生成的高级地图排除了对任何 SLAM 库存地图进行后处理的需要；此外，与传统方法相比，JISA 将所需的映射时间减少了大约 50%。在自动拼图重建中，JISA 框架优于完全自主的解决方案，以及由代理提示的按需人工干预产生的解决方案。JISA 生成的高级地图排除了对任何 SLAM 库存地图进行后期处理的需要；此外，与传统方法相比，JISA 将所需的映射时间减少了大约 50%。在自动拼图重建中，JISA 框架优于完全自主的解决方案，以及由代理提示的按需人工干预产生的解决方案。JISA 生成的高级地图排除了对任何 SLAM 库存地图进行后期处理的需要；此外，与传统方法相比，JISA 将所需的映射时间减少了大约 50%。在自动拼图重建中，JISA 框架优于完全自主的解决方案，以及由代理提示的按需人工干预产生的解决方案。