CSDF-by-SIREN: Learning Signed Distances in the Configuration Space Through Sinusoidal Representation Networks

Christoforos Vlachos, Konstantinos Moustakas

2025

Abstract

Signed Distance Functions (SDFs) are used in many fields of research. In robotics, many common tasks, such as motion planning and collision avoidance use distance queries extensively and, as a result, SDFs have been integrated widely in such tasks, fulfilling even the tightest speed requirements. At the same time, the idea of the more natural representation of distances directly in the configuration space (C-space) has been gaining ground, resulting in many interesting publications in the last few years. In this work, we aim to define a C-space Signed Distance Function (CSDF) in a way that parallels other SDF definitions. Additionally, coupled with recent advancements in machine learning and neural representation of implicit functions, we attempt to create a neural approximation of the CSDF in a way that is fast and accurate. To validate our contributions, we construct an experiment environment to test the accuracy of our proposed workflow in an inverse kinematics contact test. Comparing these results to the performance of another published approach to the neural implicit representation of distances in the Configuration Space, we found that our method offers a considerable improvement, reducing the measured errors and increasing the success rate.

Paper Citation

in Harvard Style

Vlachos C. and Moustakas K. (2025). CSDF-by-SIREN: Learning Signed Distances in the Configuration Space Through Sinusoidal Representation Networks. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 82-90. DOI: 10.5220/0013736900003982

in Bibtex Style

@conference{icinco25,
author={Christoforos Vlachos and Konstantinos Moustakas},
title={CSDF-by-SIREN: Learning Signed Distances in the Configuration Space Through Sinusoidal Representation Networks},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={82-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013736900003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - CSDF-by-SIREN: Learning Signed Distances in the Configuration Space Through Sinusoidal Representation Networks
SN - 978-989-758-770-2
AU - Vlachos C.
AU - Moustakas K.
PY - 2025
SP - 82
EP - 90
DO - 10.5220/0013736900003982
PB - SciTePress