Title: Learning to Schedule Deadline- and Operator-Sensitive Tasks,
Abstract: The use of semi-autonomous and autonomous robotic assistants to aid in care
of the elderly is expected to ease the burden on human caretakers, with
small-stage testing already occurring in a variety of countries. Yet, it is
likely that these robots will need to request human assistance via
teleoperation when domain expertise is needed for a specific task. As
deployment of robotic assistants moves to scale, mapping these requests for
human aid to the teleoperators themselves will be a difficult online
optimization problem. In this paper, we design a system that allocates requests
to a limited number of teleoperators, each with different specialities, in an
online fashion. We generalize a recent model of online job scheduling with a
worst-case competitive-ratio bound to our setting. Next, we design a scalable
machine-learning-based teleoperator-aware task scheduling algorithm and show,
experimentally, that it performs well when compared to an omniscient optimal
scheduling algorithm.