Abstract
We propose a technique to speed up the learning of the inverse kinematics of a robot manipulator by decomposing it into two or more virtual robot arms. Unlike previous decomposition approaches, this one does not place any requirement on the robot architecture and, thus, it is completely general. Parametrized Self-Organizing Maps (PSOM) are particularly adequate for this type of learning, and permit comparing results obtained directly and through the decomposition. Experimentation shows that time reductions of up to two orders of magnitude are easily attained.
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© 2005 Springer-Verlag Berlin Heidelberg
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de Angulo, V.R., Torras, C. (2005). Using PSOMs to Learn Inverse Kinematics Through Virtual Decomposition of the Robot. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_86
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DOI: https://doi.org/10.1007/11494669_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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