On the inverse kinematics of redundant manipulators
International Journal of Robotics Research
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Classifiers that approximate functions
Natural Computing: an international journal
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cognitive systems based on adaptive algorithms
ACM SIGART Bulletin
Incremental Online Learning in High Dimensions
Neural Computation
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Bounding XCS's parameters for unbalanced datasets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Context-dependent predictions and cognitive arm control with XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning sensorimotor control structures with XCSF: redundancy exploitation and dynamic control
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Control of redundant robots using learned models: an operational space control approach
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IEEE Transactions on Evolutionary Computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
Learning local linear Jacobians for flexible and adaptive robot arm control
Genetic Programming and Evolvable Machines
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This paper shows for the first time that a Learning Classifier System, namely XCSF, can learn to control a realistic arm model with four degrees of freedom in a three-dimensional workspace. XCSF learns a locally linear approximation of the Jacobian of the arm kinematics, that is, it learns linear predictions of hand location changes given joint angle changes, where the predictions are conditioned on current joint angles. To control the arm, the linear mappings are inverted--deriving appropriate motor commands given desired hand movement directions. Due to the locally linear model, the inversely desired joint angle changes can be easily derived, while effectively resolving kinematic redundancies on the fly. Adaptive PD controllers are used to finally translate the desired joint angle changes into appropriate motor commands. This paper shows that XCSF scales to three dimensional workspaces. It reliably learns to control a four degree of freedom arm in a three dimensional work space accurately and effectively while flexibly incorporating additional task constraints.