Classifiers that approximate functions
Natural Computing: an international journal
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
Applications of Learning Classifier Systems
Applications of Learning Classifier Systems
Incremental Online Learning in High Dimensions
Neural Computation
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Generalization in the XCSF Classifier System: Analysis, Improvement, and Extension
Evolutionary Computation
Mixing independent classifiers
Proceedings of the 9th 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
Analysis and Improvements of the Classifier Error Estimate in XCSF
Learning Classifier Systems
Learning sensorimotor control structures with XCSF: redundancy exploitation and dynamic control
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
From Motor Learning to Interaction Learning in Robots
From Motor Learning to Interaction Learning in Robots
A comparative study: function approximation with LWPR and XCSF
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Modularization of xcsf for multiple output dimensions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
IEEE Transactions on Evolutionary Computation
Learning local linear Jacobians for flexible and adaptive robot arm control
Genetic Programming and Evolvable Machines
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It was previously shown that the control of a robot arm can be efficiently learned using the XCSF classifier system. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we exploit the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated, kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF predictions maybe underestimated, in which case self-delusional spiraling effects hinder effective learning. Thus, we introduce a heuristic parameter, which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance coping with more than ten times higher noise levels.