  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.
