Exploring human-robot cooperation with gamified user training: a user study on cooperative lifting.

HRC IMU co-lift gamification human motion tracking user training

Journal

Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350

Informations de publication

Date de publication:
2023
Historique:
received: 06 09 2023
accepted: 05 12 2023
medline: 29 1 2024
pubmed: 29 1 2024
entrez: 29 1 2024
Statut: epublish

Résumé

Human-robot cooperation (HRC) is becoming increasingly relevant with the surge in collaborative robots (cobots) for industrial applications. Examples of humans and robots cooperating actively on the same workpiece can be found in research labs around the world, but industrial applications are still mostly limited to robots and humans taking turns. In this paper, we use a cooperative lifting task (co-lift) as a case study to explore how well this task can be learned within a limited time, and how background factors of users may impact learning. The experimental study included 32 healthy adults from 20 to 54 years who performed a co-lift with a collaborative robot. The physical setup is designed as a gamified user training system as research has validated that gamification is an effective methodology for user training. Human motions and gestures were measured using Inertial Measurement Unit (IMU) sensors and used to interact with the robot across three role distributions: human as the leader, robot as the leader, and shared leadership. We find that regardless of age, gender, job category, gaming background, and familiarity with robots, the learning curve of all users showed a satisfactory progression and that all users could achieve successful cooperation with the robot on the co-lift task after seven or fewer trials. The data indicates that some of the background factors of the users such as occupation, past gaming habits,

Identifiants

pubmed: 38283803
doi: 10.3389/frobt.2023.1290104
pii: 1290104
pmc: PMC10811071
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1290104

Informations de copyright

Copyright © 2024 Venås, Stølen and Kyrkjebø.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Gizem Ateş Venås (GA)

Department of Computer Science, Electrical Engineering and Mathematical Sciences, Førde, Norway.

Martin Fodstad Stølen (MF)

Department of Computer Science, Electrical Engineering and Mathematical Sciences, Førde, Norway.

Erik Kyrkjebø (E)

Department of Computer Science, Electrical Engineering and Mathematical Sciences, Førde, Norway.

Classifications MeSH