Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
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
State of XCS Classifier System Research
Learning Classifier Systems, From Foundations to Applications
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
Incremental Online Learning in High Dimensions
Neural Computation
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation
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
Classifier Conditions Using Gene Expression Programming
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
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on 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
Controlling a four degree of freedom arm in 3D using the XCSF learning classifier system
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
A comparative study: function approximation with LWPR and XCSF
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Resource management and scalability of the XCSF learning classifier system
Theoretical Computer Science
Goal Babbling Permits Direct Learning of Inverse Kinematics
IEEE Transactions on Autonomous Mental Development
IEEE Transactions on Evolutionary Computation
Filtering sensory information with XCSF: improving learning robustness and control performance
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Self organizing classifiers and niched fitness
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Successful planning and control of robots strongly depends on the quality of kinematic models, which define mappings between configuration space (e.g. joint angles) and task space (e.g. Cartesian coordinates of the end effector). Often these models are predefined, in which case, for example, unforeseen bodily changes may result in unpredictable behavior. We are interested in a learning approach that can adapt to such changes--be they due to motor or sensory failures, or also due to the flexible extension of the robot body by, for example, the usage of tools. We focus on learning locally linear forward velocity kinematics models by means of the neuro-evolution approach XCSF. The algorithm learns self-supervised, executing movements autonomously by means of goal-babbling. It preserves actuator redundancies, which can be exploited during movement execution to fulfill current task constraints. For detailed evaluation purposes, we study the performance of XCSF when learning to control an anthropomorphic seven degrees of freedom arm in simulation. We show that XCSF can learn large forward velocity kinematic mappings autonomously and rather independently of the task space representation provided. The resulting mapping is highly suitable to resolve redundancies on the fly during inverse, goal-directed control.