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.


generalisation (artificial intelligence), learning (artificial intelligence), manipulators, robots.

Author keywords

Function approximation, learning inverse kinematics, parametrized self-organizing maps (PSOMs), robot kinematics

Scientific reference

V. Ruiz de Angulo and C. Torras. Learning inverse kinematics: Reduced sampling through decomposition into virtual robots. IEEE Transactions on Systems, Man and Cybernetics: Part B, 38(6): 1571-1577, 2008.