Improving Human Performance Using Mixed Granularity of Control in Multi-Human Multi-Robot Interaction

Due to the potentially large number of units involved, the interaction with a\nmulti-robot system is likely to exceed the limits of the span of apprehension\nof any individual human operator. In previous work, we studied how this issue\ncan be tackled by interacting with the robots in two modalities --\nenvironment-oriented and robot-oriented. In this paper, we study how this\nconcept can be applied to the case in which multiple human operators perform\nsupervisory control on a multi-robot system. While the presence of extra\noperators suggests that more complex tasks could be accomplished, little\nresearch exists on how this could be achieved efficiently. In particular, one\nchallenge arises -- the out-of-the-loop performance problem caused by a lack of\nengagement in the task, awareness of its state, and trust in the system and in\nthe other operators. Through a user study involving 28 human operators and 8\nreal robots, we study how the concept of mixed granularity in multi-human\nmulti-robot interaction affects user engagement, awareness, and trust while\nbalancing the workload between multiple operators.\n