CoAx: Collaborative Action Dataset for Human Motion Forecasting in an Industrial Workspace

Dimitrios Lagamtzis, Fabian Schmidt, Jan Seyler, Thao Dang

2022

Abstract

Human robot collaboration in industrial workspaces where humans perform challenging assembly tasks has become too much; increasingly popular. Now that intention recognition and motion forecasting is being more and more successful in different research fields, we want to transfer that success (and the algorithms making this success possible) to human motion forecasting in an industrial context. Therefore, we present a novel public dataset comprising several industrial assembly tasks, one of which incorporates interaction with a robot. The dataset covers 3 industrial work tasks with robot interaction performed by 6 subjects with 10 repetitions per subject summing up to 1 hour and 58 minutes of video material. We also evaluate the dataset with two baseline methods. One approach is solely velocity-based and the other one is using timeseries classification to infer the future motion of the human worker.

Paper Citation

in Harvard Style

Lagamtzis D., Schmidt F., Seyler J. and Dang T. (2022). CoAx: Collaborative Action Dataset for Human Motion Forecasting in an Industrial Workspace. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 98-105. DOI: 10.5220/0010775600003116

in Bibtex Style

@conference{icaart22,
author={Dimitrios Lagamtzis and Fabian Schmidt and Jan Seyler and Thao Dang},
title={CoAx: Collaborative Action Dataset for Human Motion Forecasting in an Industrial Workspace},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={98-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010775600003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - CoAx: Collaborative Action Dataset for Human Motion Forecasting in an Industrial Workspace
SN - 978-989-758-547-0
AU - Lagamtzis D.
AU - Schmidt F.
AU - Seyler J.
AU - Dang T.
PY - 2022
SP - 98
EP - 105
DO - 10.5220/0010775600003116