Learning to Schedule Deadline- and Operator-Sensitive Tasks
This addresses the challenge of efficiently allocating human assistance to semi-autonomous robots in elderly care, though it is incremental as it builds on existing scheduling models.
The paper tackles the problem of scheduling tasks for robotic assistants that require human teleoperators with different specialties, by designing a machine-learning-based algorithm that performs well compared to an optimal scheduler in experiments.
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.