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Human-robot collaboration frequently requires extensive communication, e.g.,using natural language and gestures. Augmented reality (AR) has provided analternative way of bridging the communication gap between robots and people.However, most current AR-based human-robot communication methods areunidirectional, focusing on how the human adapts to robot behaviors, and arelimited to single-robot domains. In this paper, we develop AR for RobotsCollaborating with a Human (ARROCH), a novel algorithm and system that supportsbidirectional, multi-turn, human-multi-robot communication in indoor multi-roomenvironments. 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 havebeen conducted with real robots and human participants using collaborativedelivery tasks. Results show that ARROCH outperformed a standard non-ARapproach in both user experience and teamwork efficiency. In addition, we havedeveloped a novel simulation environment using Unity (for AR and humansimulation) and Gazebo (for robot simulation). Results in simulationdemonstrate ARROCH's superiority over AR-based baselines in human-robotcollaboration.

ARROCH: Augmented Reality for Robots Collaborating with a Human