Unsupervised Learning of Assistive Camera Views by an Aerial Co-robot in Augmented Reality Multitasking Environments

Abstract

This paper presents a novel method by which an assistive aerial robot can learn the relevant camera views within a task domain through tracking the head motions of a human collaborator. The human's visual field is modeled as an anisotropic spherical sensor, which decays in acuity towards the periphery, and is integrated in time throughout the domain. This data is resampled and fed into an expectation maximization solver in order to estimate the environment's visual interest as a mixture of Gaussians. A dynamic coverage control law directs the robot to capture camera views of the peaks of these Gaussians which is broadcast to an augmented reality display worn by the human operator. An experimental study is presented that assesses the influence of the assistive robot on reflex time, head motion, and task completion time.