Personalized Augmented Reality Via Fog-Based Imitation Learning

Augmented reality (AR) technologies are rapidly gaining momentum in society and are expected to play a critical role in the future of cities and transportation. In such dynamic settings with a heterogeneous population of AR users, it is important for holograms to be placed in the surrounding environment with regard to the users' preferences. However, the area of AR personalization remains largely unexplored. This paper proposes to use behavioral cloning, an algorithm for imitation learning, as a means of automatically generating policies that capture user preferences of hologram positioning. We argue in favor of employing the fog computing paradigm to minimize the volume of data sent to the cloud, and thereby preserve user privacy and increase both communication efficiency and learning efficiency. Through preliminary results obtained with a custom, Unity-based AR simulator, we demonstrate that user-specific policies can be learned quickly and accurately.