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Authors: Dimitrios Lagamtzis 1 ; Fabian Schmidt 1 ; Jan Seyler 2 and Thao Dang 1

Affiliations: 1 Department of Computer Science and Engineering, Esslingen University, Esslingen, Germany ; 2 Festo SE & Co. KG, Esslingen, Germany

Keyword(s): Human Robot Collaboration, Industrial Assembly Dataset, Human Motion Forecasting, Action Recognition.

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.

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 98-105. DOI: 10.5220/0010775600003116

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Lagamtzis, D.
AU - Schmidt, F.
AU - Seyler, J.
AU - Dang, T.
PY - 2022
SP - 98
EP - 105
DO - 10.5220/0010775600003116
PB - SciTePress

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