Behavior Detection of Quadruped Companion Robots Using CNN: Towards Closer Human-Robot Cooperation

Piotr Artiemjew, Karolina Krzykowska-Piotrowska, Marek Piotrowski

2025

Abstract

In a world where mobile robotics is increasingly entering various areas of people’s lives, creating systems that track the behavior of mobile robots is a natural step toward ensuring their proper functioning. This is particularly important in cases where improper use or unpredictable behavior may pose a threat to the environment and, above all, to humans. It should be emphasized that this is especially relevant in the context of using robotic solutions to improve the quality of life for people with special needs, as well as in human–robot interaction. Our primary aim was to verify the experimental effectiveness of classification based on convo-lutional neural networks for detecting behaviours of four-legged robots. The study focused on evaluating the performance in recognising typical robot poses. The research was conducted in our robotics laboratory, using Spot and Unitree Go2 Pro quadruped robots as experimental platforms. We addressed the challenging task of pose recognition without relying on motion tracking — a difficulty particularly pronounced when dealing with rotations.

Paper Citation

in Harvard Style

Artiemjew P., Krzykowska-Piotrowska K. and Piotrowski M. (2025). Behavior Detection of Quadruped Companion Robots Using CNN: Towards Closer Human-Robot Cooperation. In Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-757-3, SciTePress, pages 281-287. DOI: 10.5220/0013548200003964

in Bibtex Style

@conference{icsoft25,
author={Piotr Artiemjew and Karolina Krzykowska-Piotrowska and Marek Piotrowski},
title={Behavior Detection of Quadruped Companion Robots Using CNN: Towards Closer Human-Robot Cooperation},
booktitle={Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2025},
pages={281-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013548200003964},
isbn={978-989-758-757-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Behavior Detection of Quadruped Companion Robots Using CNN: Towards Closer Human-Robot Cooperation
SN - 978-989-758-757-3
AU - Artiemjew P.
AU - Krzykowska-Piotrowska K.
AU - Piotrowski M.
PY - 2025
SP - 281
EP - 287
DO - 10.5220/0013548200003964
PB - SciTePress