  In human-robot collaboration domains, augmented reality (AR) technologies
have enabled people to visualize the state of robots. Current AR-based
visualization policies are designed manually, which requires a lot of human
efforts and domain knowledge. When too little information is visualized, human
users find the AR interface not useful; when too much information is
visualized, they find it difficult to process the visualized information. In
this paper, we develop a framework, called VARIL, that enables AR agents to
learn visualization policies (what to visualize, when, and how) from
demonstrations. We created a Unity-based platform for simulating warehouse
environments where human-robot teammates collaborate on delivery tasks. We have
collected a dataset that includes demonstrations of visualizing robots' current
and planned behaviors. Results from experiments with real human participants
show that, compared with competitive baselines from the literature, our learned
visualization strategies significantly increase the efficiency of human-robot
teams, while reducing the distraction level of human users. VARIL has been
demonstrated in a built-in-lab mock warehouse.
