A Framework on Division of Work Tasks between Humans and Robots in the Home
Academic article
Published version
View/ Open
Date
2020-07-27Metadata
Show full item recordCollections
Original version
Saplacan D, Herstad J, Tørresen J, Pajalic Z. A Framework on Division of Work Tasks between Humans and Robots in the Home. Multimodal Technologies and Interaction. 2020;4(3)Abstract
This paper analyzes work activity in the home, e.g., cleaning, performed by two actors, a human and a robot. Nowadays, there are attempts to automate this activity through the use of robots. However, the activity of cleaning, in and of itself, is not important; it is used instrumentally to understand if and how robots can be integrated within current and future homes. The theoretical framework of the paper is based on empirical work collected as part of the Multimodal Elderly Care Systems (MECS) project. The study proposes a framework for the division of work tasks between humans and robots. The framework is anchored within existing research and our empirical findings. Swim-lane diagrams are used to visualize the tasks performed (WHAT), by each of the two actors, to ascertain the tasks’ temporality (WHEN), and their distribution and transitioning from one actor to the other (WHERE). The study presents the framework of various dimensions of work tasks, such as the types of work tasks, but also the temporality and spatiality of tasks, illustrating linear, parallel, sequential, and distributed tasks in a shared or non-shared space. The study’s contribution lies in its foundation for analyzing work tasks that robots integrated into or used in the home may generate for humans, along with their multimodal interactions. Finally, the framework can be used to visualize, plan, and design work tasks for the human and for the robot, respectively, and their work division.
Publisher
MDPISeries
Multimodal Technologies and Interaction;Volume 4, Issue 3Journal
Multimodal Technologies and Interaction
Except where otherwise noted, this item's license is described as © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).