# Learning to Schedule Deadline- and Operator-Sensitive Tasks

## TL;DR

This paper presents a scalable, machine-learning-based scheduling system for allocating robot-initiated human assistance requests to specialized teleoperators, optimizing online task assignment in elderly care robotics.

## Contribution

It introduces a novel online scheduling algorithm that accounts for teleoperator specialties and demonstrates its effectiveness through experimental evaluation.

## Key findings

- The proposed algorithm performs close to an omniscient optimal scheduler.
- It generalizes a recent online job scheduling model with a worst-case competitive ratio.
- Experimental results show scalability and efficiency in real-world scenarios.

## 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.