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.

This paper presents a novel method for generating the trajectory of a robot assisting a human in servicing a set of tasks embedded in a convex 2-D domain. This method makes use of Bayesian inference to predict human intent in task selection. Rather than following optimal trajectory towards a single task, the robot computes a set of potentially optimal tasks each weighted by the human's posterior probability and superimposes them into a cost function that is designed to minimize the weighted Euclidean distance relative to set. The effect is a flexible path human-robot collaborative network that is shown in simulation to complete all tasks in a given domain in less time than existing methods for a certain class of highly impulsive humans, i.e., humans that tend to randomly switch tasks at times generated by a Poisson counting process. The algorithm is also illustrated through an experimental demonstration.