A Task Allocation Framework for Human Multi-Robot Collaborative Settings

TL;DR

This paper introduces a task allocation framework for human multi-robot teams that optimizes efficiency and human satisfaction by combining offline planning with online reallocation, considering human preferences and unforeseen events.

Contribution

It proposes a novel hybrid task allocation framework that integrates offline optimization with online adjustments tailored for human-robot collaboration.

Findings

Improved team efficiency in cooperative tasks.

Enhanced human satisfaction through preference-aware reallocation.

Effective handling of unforeseen events in task execution.

Abstract

The requirements of modern production systems together with more advanced robotic technologies have fostered the integration of teams comprising humans and autonomous robots. However, along with the potential benefits also comes the question of how to effectively handle these teams considering the different characteristics of the involved agents. For this reason, this paper presents a framework for task allocation in a human multi-robot collaborative scenario. The proposed solution combines an optimal offline allocation with an online reallocation strategy which accounts for inaccuracies of the offline plan and/or unforeseen events, human subjective preferences and cost of switching from one task to another so as to increase human satisfaction and team efficiency. Experiments are presented for the case of two manipulators cooperating with a human operator for performing a box filling task.