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TSMC
2008

Learning Inverse Kinematics: Reduced Sampling Through Decomposition Into Virtual Robots

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Learning Inverse Kinematics: Reduced Sampling Through Decomposition Into Virtual Robots
We propose a technique to speedup 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 are particularly adequate for this type of learning, and permit comparing results directly obtained and through the decomposition. Experimentation shows that time reductions of up to two orders of magnitude are easily attained.
Vicente Ruiz de Angulo, Carme Torras
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TSMC
Authors Vicente Ruiz de Angulo, Carme Torras
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