Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Genetic programming: on the programming of computers by means of natural selection
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Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Biology and Technology of Intelligent Autonomous Agents
Biology and Technology of Intelligent Autonomous Agents
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Recent Advances in Robot Learning
Recent Advances in Robot Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Making Robots Smarter: Combining Sensing and Action through Robot Learning
Making Robots Smarter: Combining Sensing and Action through Robot Learning
Self-Organizing Maps
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Model complexity control and statisticallearning theory
Natural Computing: an international journal
Assessing Image Features for Vision-Based Robot Positioning
Journal of Intelligent and Robotic Systems
Neural computing increases robot adaptivity
Natural Computing: an international journal
Proceedings of the First European Workshop on Evolutionary Robotics
Proceedings of the First European Workshop on Evolutionary Robotics
Sequential Learning in Feedforward Networks: Proactive and Retroactive Interference Minimization
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Learning Inverse Kinematics via Cross-Point Function Decomposition
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Neural Learning Invariant to Network Size Changes
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Neural learning methods yielding functional invariance
Theoretical Computer Science
Architecture-Independent Approximation of Functions
Neural Computation
A framework to deal with interference in connectionist systems
AI Communications
Using PSOMs to learn inverse kinematics through virtual decomposition of the robot
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-calibration of a space robot
IEEE Transactions on Neural Networks
Speeding up the learning of robot kinematics through function decomposition
IEEE Transactions on Neural Networks
On-line learning with minimal degradation in feedforward networks
IEEE Transactions on Neural Networks
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The biological world offers a full range of adaptive mechanisms, from which technology researchers try to get inspiration. Among the several disciplines attempting to reproduce these mechanisms artificially, this paper concentrates on the field of Neural Networks and its contributions to attain sensorimotor adaptivity in robots. Essentially this type of adaptivity requires tuning nonlinear mappings on the basis of input-output information. Several experimental robotic systems are described, which rely on inverse kinematics and visuomotor mappings. Finally, the main trends in the evolution of neural computing are highlighted, followed by some remarks drawn from the surveyed robotic applications.