Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Learning Inverse Kinematics via Cross-Point Function Decomposition
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Self-calibration of a space robot
IEEE Transactions on Neural Networks
Speeding up the learning of robot kinematics through function decomposition
IEEE Transactions on Neural Networks
Natural inspiration for artificial adaptivity: some neurocomputing experiences in robotics
UC'05 Proceedings of the 4th international conference on Unconventional Computation
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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.