%0 Journal Article %1 DSouza:2001 %A D'Souza, A. %A Vijayakumar, S. %A Schaal, S. %D 2001 %J IEEE International Conference on Intelligent Robots and Systems (IROS 2001) %K control dimensional high humanoid inverse kinematics learning local motion null optimization rate resolved robotics space %P 298-303 %T Learning inverse kinematics %V 1 %X Real-time control of the endeffector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates learning of inverse kinematics for resolved motion rate control (RMRC) employing an optimization criterion to resolve kinematic redundancies. Our learning approach is based on the key observations that learning an inverse of a non uniquely invertible function can be accomplished by augmenting the input representation to the inverse model and by using a spatially localized learning approach. We apply this strategy to inverse kinematics learning and demonstrate how a recently developed statistical learning algorithm, Locally Weighted Projection Regression, allows efficient learning of inverse kinematic mappings in an incremental fashion even when input spaces become rather high dimensional. The resulting performance of the inverse kinematics is comparable to Liegeois (1) analytical pseudo inverse with optimization. Our results are illustrated with a 30 degree-of-freedom humanoid robot.

@article{DSouza:2001, abstract = {Real-time control of the endeffector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates learning of inverse kinematics for resolved motion rate control (RMRC) employing an optimization criterion to resolve kinematic redundancies. Our learning approach is based on the key observations that learning an inverse of a non uniquely invertible function can be accomplished by augmenting the input representation to the inverse model and by using a spatially localized learning approach. We apply this strategy to inverse kinematics learning and demonstrate how a recently developed statistical learning algorithm, Locally Weighted Projection Regression, allows efficient learning of inverse kinematic mappings in an incremental fashion even when input spaces become rather high dimensional. The resulting performance of the inverse kinematics is comparable to Liegeois ([1]) analytical pseudo inverse with optimization. Our results are illustrated with a 30 degree-of-freedom humanoid robot.}, added-at = {2009-06-26T15:25:19.000+0200}, author = {D'Souza, A. and Vijayakumar, S. and Schaal, S.}, biburl = {https://www.bibsonomy.org/bibtex/26f7fce9cc6d720704e9d17311bd50622/butz}, description = {diverse cognitive systems bib}, interhash = {697e45ad5ffbc5ff190ae675769e5fb1}, intrahash = {6f7fce9cc6d720704e9d17311bd50622}, journal = {IEEE International Conference on Intelligent Robots and Systems (IROS 2001)}, keywords = {control dimensional high humanoid inverse kinematics learning local motion null optimization rate resolved robotics space}, owner = {butz}, pages = {298-303}, timestamp = {2009-06-26T15:25:26.000+0200}, title = {Learning inverse kinematics}, volume = 1, year = 2001 }