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Authors: Sebastian Höfer and Manfred Hild

Affiliation: Humboldt-Universität zu Berlin, Germany

Keyword(s): Slow feature analysis, Biped walking, Volterra filters, Recurrent neural networks, Sensorimotor control, Humanoid robotics.

Related Ontology Subjects/Areas/Topics: Adaptive Architectures and Mechanisms ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bio-Inspired and Humanoid Robotics ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: This paper presents an approach for increasing the reactivity of a humanoid robot’s gait, incorporating Slow Feature Analysis (SFA), an unsupervised learning algorithm issuing from the domain of theoretical biology. The main objective of this work is to find a means to detect disturbances in the gait pattern at an early stage without losing stability. Another goal is to investigate the general potential of SFA for using it within sensorimotor loops which to our knowledge has not been considered until now. The application of SFA within sensorimotor loops is motivated by pointing out its relation to second-order Volterra filters. Our experiments show that the overall reactivity of the gait pattern increases without any profound loss in stability, and that SFA appears to be suitable for the usage even at such levels of sensorimotor control that are directly involved into motor activity regulation.

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Paper citation in several formats:
Höfer, S. and Hild, M. (2010). USING SLOW FEATURE ANALYSIS TO IMPROVE THE REACTIVITY OF A HUMANOID ROBOT'S SENSORIMOTOR GAIT PATTERN. In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC; ISBN 978-989-8425-32-4, SciTePress, pages 212-219. DOI: 10.5220/0003082102120219

@conference{icnc10,
author={Sebastian Höfer. and Manfred Hild.},
title={USING SLOW FEATURE ANALYSIS TO IMPROVE THE REACTIVITY OF A HUMANOID ROBOT'S SENSORIMOTOR GAIT PATTERN},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC},
year={2010},
pages={212-219},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003082102120219},
isbn={978-989-8425-32-4},
}

TY - CONF

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC
TI - USING SLOW FEATURE ANALYSIS TO IMPROVE THE REACTIVITY OF A HUMANOID ROBOT'S SENSORIMOTOR GAIT PATTERN
SN - 978-989-8425-32-4
AU - Höfer, S.
AU - Hild, M.
PY - 2010
SP - 212
EP - 219
DO - 10.5220/0003082102120219
PB - SciTePress