System identification: theory for the user
System identification: theory for the user
A survey of general-purpose manipulation
International Journal of Robotics Research
Topology-conserving maps for learning visuo-motor-coordination
Neural Networks
Local dimensionality reduction
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Learning nonlinear dynamical systems using an EM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Self-Organizing Maps
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Applied 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
Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators
Journal of Intelligent and Robotic Systems
Local Dimensionality Reduction for Locally Weighted Learning
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Accurate on-line support vector regression
Neural Computation
A tutorial on support vector regression
Statistics and Computing
Neural Computation
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Classifier fitness based on accuracy
Evolutionary Computation
Learning to Control in Operational Space
International Journal of Robotics Research
Adaptive Filters
Context-dependent predictions and cognitive arm control with XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Library for Locally Weighted Projection Regression
The Journal of Machine Learning Research
Adaptive Optimal Control for Redundantly Actuated Arms
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Robotics: Modelling, Planning and Control
Robotics: Modelling, Planning and Control
Learning sensorimotor control structures with XCSF: redundancy exploitation and dynamic control
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Active learning with statistical models
Journal of Artificial Intelligence Research
Active learning for sensorimotor coordinations of autonomous robots
HSI'09 Proceedings of the 2nd conference on Human System Interactions
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
Bounding the population size in XCS to ensure reproductive opportunities
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Robot Programming by Demonstration
Robot Programming by Demonstration
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
Online incremental learning of inverse dynamics incorporating prior knowledge
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Learning multiple models of non-linear dynamics for control under varying contexts
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
R-IAC: Robust Intrinsically Motivated Exploration and Active Learning
IEEE Transactions on Autonomous Mental Development
Body Schema in Robotics: A Review
IEEE Transactions on Autonomous Mental Development
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
IEEE Transactions on Robotics
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
Neural-network control of mobile manipulators
IEEE Transactions on Neural Networks
Three-dimensional neural net for learning visuomotor coordination of a robot arm
IEEE Transactions on Neural Networks
Locally Weighted Interpolating Growing Neural Gas
IEEE Transactions on Neural Networks
Implementation of self-organizing neural networks for visuo-motor control of an industrial robot
IEEE Transactions on Neural Networks
Filtering sensory information with XCSF: improving learning robustness and control performance
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
Robotics and Autonomous Systems
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With the emergence of more challenging contexts for robotics, the mechanical design of robots is becoming more and more complex. Moreover, their missions often involve unforeseen physical interactions with the environment. To deal with these difficulties, endowing the controllers of the robots with the capability to learn a model of their kinematics and dynamics under changing circumstances is becoming mandatory. This emergent necessity has given rise to a significant amount of research in the Machine Learning community, generating algorithms that address more and more sophisticated on-line modeling questions. In this paper, we provide a survey of the corresponding literature with a focus on the methods rather than on the results. In particular, we provide a unified view of all recent algorithms that outlines their distinctive features and provides a framework for their combination. Finally, we give a prospective account of the evolution of the domain towards more challenging questions.