  Human-robot collaboration frequently requires extensive communication, e.g.,
using natural language and gestures. Augmented reality (AR) has provided an
alternative way of bridging the communication gap between robots and people.
However, most current AR-based human-robot communication methods are
unidirectional, focusing on how the human adapts to robot behaviors, and are
limited to single-robot domains. In this paper, we develop AR for Robots
Collaborating with a Human (ARROCH), a novel algorithm and system that supports
bidirectional, multi-turn, human-multi-robot communication in indoor multi-room
environments. The human can see through obstacles to observe the robots'
current states and intentions, and provide feedback, while the robots'
behaviors are then adjusted toward human-multi-robot teamwork. Experiments have
been conducted with real robots and human participants using collaborative
delivery tasks. Results show that ARROCH outperformed a standard non-AR
approach in both user experience and teamwork efficiency. In addition, we have
developed a novel simulation environment using Unity (for AR and human
simulation) and Gazebo (for robot simulation). Results in simulation
demonstrate ARROCH's superiority over AR-based baselines in human-robot
collaboration.
