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| Record Number | 1454 |
| Reference Type | Conference Proceedings |
| Author(s) | D'Souza, A.;Vijayakumar, S.;Schaal, S. |
| Year | 2001 |
| Title | Learning inverse kinematics |
| Journal/Conference/Book Title | IEEE International Conference on Intelligent Robots and Systems (IROS 2001) |
| Keywords | inverse kinematics, humanoid robotics, high dimensional, resolved motion rate control, local learning, null space optimization |
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.
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| Notes | clmc |
| URL(s) | http://www-clmc.usc.edu/publications/D/dsouza-IROS2001.pdf
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| Place Published | Maui, Hawaii, Oct.29-Nov.3 |
| Publisher | Piscataway, NJ: IEEE |
| Short Title | Learning inverse kinematics |
| Papers are available as Adobe PDF ".pdf" files. Adobe Reader is available for free for all computer platforms.
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Page last modified on August 10, 2006, at 06:47 PM
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